Table 8.

Opportunities and threats for digital innovations.

Technology	Opportunity	Threat
mHealth	Wide user basis of mobile phone users [49,50] Rapid growth in the number of applications supporting self-management [51,52,53] Applicable to a wide scope of diagnoses [47,53] Increased patient engagement during treatment [47,52,53,54,55]	Ethical and legal aspects [53,56,57,58] Limited evidence of outcomes and benefits (insufficient randomised controlled trials) [47,52,56,59,60] Low interoperability and integration with existing work procedures [56] Uncertainty concerning data reliability [47,56] Declining patient self-discipline over time [52] Absence of personal contact with physician [55] Non-certified applications, large number of applications [61,62] Level of physician acceptance of mobile health applications [62]
Electronic Health Record (EHR), Electronic Medical Record (EMR), Personal Health Record (PHR)	Access to information for all stakeholders [63,64,65,66,67] Benefits if combined with AI [58,65,68] Higher accuracy, legibility, reliability, and better information search functions [64,65,69,70] Risk management—reminders, warnings (allergies, patient history) [64,67,70] Less burden on treating medical staff [36,64] Reduction of cost related to poor documentation [64,65,69]	Violation of the interoperability condition [53,63,70,71] Problem with aligning operating standards with the current information exchange protocols for Big Data [72] Regulatory restraints [72,73,74] The risk of possible re-identification [74] Financial sustainability [75]
Digital biomarkers	Wide user base [76] Wide range of information [76] Better diagnostic and decision-making on interventions thanks to continual data collection [58,59] Developing flexible electronic materials for integrating chip technology [77,78]	Bad choice of monitored attributes [59] Problems with technology validation [59]
Telemedicine	Lower risk of disease transmission [79,80,81] Suitable for “social distancing” [82] Reduction in hospitalization cost [83,84] Comparable or better care than that of in-person consultations [79,83,85] Elimination of the feeling of isolation during hospitalization [79] Alleviation of resource scarcity (staff, geographical location) [84,86,87,88] Shorter waiting times [60,86] Applicable to numerous diagnoses (e.g., in psychiatry, dermatology, etc.) [60,89,90,91,92]	Limited applicability based on diagnosis [79,85] Unreliable Internet connection [79,85] Lack of training in the use of digital devices [60,79,93] Violation of interoperability between healthcare providers and healthcare systems [94] Discrimination of certain patient groups (e.g., people with particular handicaps) [80] Limited evidence of outcomes and benefits (insufficient randomised controlled trials) [60,80]
Artificial intelligence (AI)	Prediction of illness development [94,95,96,97,98] Improvements in treatment optimization and effectiveness [94,97,99,100] Evidence-based recommendations [60,98,101] Delegation of simple and repeating tasks to AI [96] Lower number of hospitalizations [95] Cost cutting [77,95,97] Less pressure on scarce HR in healthcare [102,103] Automatic recall and rescheduling of patients [98] Bigger potential of other digital innovations [68,104] Ability to process huge amounts of data [101] AI-biosensors (miniaturization, scalability, low power consumption, high sensitivity, multifunction, safety, non-toxicity, and degradation) [77]	Incompatible with older infrastructure [105] Lack of understanding of AI functionality [68,106] Inefficient use of AI in day-to-day workflows [107,108] Potential conflict between human ability to act autonomously and the complicated, allegedly infallible machine logic (known as automation bias [69,100] Legal and ethical issues [68,95,100,101,104] Physicians’ concern about AI (security, privacy, and confidentiality) [68,101] Missing multidisciplinary AI teams [98]
Wearable technologies	Wide user base [76,77,93] Better diagnostics and decision-making about interventions thanks to continual data collection [76,77,91,106,109] Source of objective data (measured in real-life conditions) [76,91,110] Reduction of “unnecessary” out-patient visits [94] 4P medicine (predictive, precise, preventative and personalized) [76,77,111]	Data smog [76,91] Standardization and validation issues with sensor placement [91] Energy consumption (limited battery capacity) [49,84] Different levels of digital literacy and/or aproach to technologies among patients [47,91] Declining patient self-discipline over time [91] Limited availability due to high production costs of some technologies [77]
Internet of Things (IoT)	Higher operational efficiency [49,112] Integration of data from various sources [49,112] Disease prevention and monitoring [49,113] Use of AI in analyses [49,65,113]	Loss of safe and stable communication with devices [84] Higher demands on network infrastructure [49] Unauthorised manipulation [49,112] There are currently no clear instructions for healthcare staff how to use IoT (e.g., in recommendations to patients concerning their use) [49]