Digital health and patient‐centred care: A digital systems view

Abstract

The public sectors' shift to digital first service provision has had a considerable impact on how individuals interact with public sector entities. This research highlights the similar assistance requirements and concerns with different public sector digital services. Evidence for this research is presented through a case study on the Australian digital healthcare platform, MyAgedCare. By understanding the different issues and assistance seeking requirements across the public sector digital services, digital service designers and policymakers can better create services that meet the needs and expectations of users. A primary finding of this research highlights the expectation of users that the human interface for assistance is maintained, in order to maximize an individual's capacity to interact with the system successfully.

Health literacy and older adults: Findings from a national population‐based survey

“…44 These barriers may further have been compounded by the sharp rise in digital health communication and service provision in part due to the COVID-19 pandemic, and thus further exploration is warranted. 45 Another novel finding in our analysis is the link between country of birth, English proficiency and health literacy. The data suggest that the country of birth has less effect on health literacy than English proficiency.…”

Artificial Intelligence‐Based Medical Sensors for Healthcare System

“…Nowadays, it is shifting the focus to the individualcentered healthcare system based on big data, emphasizing early detection of risk factors, early diagnosis, and early preventive treatment. [111][112][113][114][115][116][117] On one hand, AI algorithms have the ability to merge deep analysis with powerful predictive capabilities, providing fast disease predictions for future data through large amount of medical data processing and model training. On the other hand, CDSS helps the decision-makers and healthcare systems to improve the information, insights, and environments approaching way.…”

Artificial Intelligence in Medical Sensors for Clinical Decisions

“…Researchers used the end-to-end deep-learning model to predict whether a patient had lung cancer, generating a patient lung cancer malignancy risk score and identifying the location of the malignant tissue in the lungs. Novel digital AI-enabled CDSSs accessible through clinicians’ smart devices will replace the outdated waiting room clipboard screening questionnaires and help overcome the relative prevalence of diagnostic errors . As a result, healthcare providers will be further motivated to invest in new point-of-care information tools and to adopt new methods to aid diagnostic reasoning, resulting in fewer patients being misdiagnosed every year.…”

“…Novel digital AIenabled CDSSs accessible through clinicians' smart devices will replace the outdated waiting room clipboard screening questionnaires and help overcome the relative prevalence of diagnostic errors. 97 As a result, healthcare providers will be further motivated to invest in new point-of-care information tools and to adopt new methods to aid diagnostic reasoning, resulting in fewer patients being misdiagnosed every year. An AI-enabled CDSS will redesign clinic processes to establish a closed-loop system, processing electronic records through decision support; using mnemonics, checklists, and online support to generate appropriately differential diagnostics; 17,21,34 building error-reporting systems to encourage learning from errors; reducing the cognitive load for providers to ensure rapid access to information; integrating data from new diagnostic tests in primary care; and providing new system designs by accounting for human factors.…”