Currently accepted at: Journal of Medical Internet Research
Date Submitted: Feb 4, 2020
Open Peer Review Period: Feb 4, 2020 - Mar 30, 2020
Date Accepted: Apr 27, 2020
Date Submitted to PubMed: Jul 14, 2020
(closed for review but you can still tweet)
Improving the primary care consultation through digital medical interview assistant systems - the cases of diabetes and depression: A narrative review
Digital medical interview assistant (DMIA, also known as computer-assisted history taking (CAHT)) systems have the potential to improve the quality of care and the medical consultation by exploring a larger number of aspects related to the patient without time constraints, and therefore acquiring more and better quality information, prior to the face-to-face consultation. The consultation in primary care (PC) is the broadest in terms of the amount of topics to be covered and, at the same time, the shortest in term of time spent with the patient. In this study, we explore how DMIA systems may be used specifically in the context of PC, to improve the consultations for diabetes and depression, as exemplars for chronic conditions. A narrative review was conducted focusing on (1) the characteristics of the primary care consultation in general and for diabetes and depression specifically, and on (2) the impact of DMIA/CAHT systems on the medical consultation. Through thematic analysis, we identified the characteristics of the PC consultation that a DMIA system would be able to improve and developed a sample questionnaire for diabetes and depression to illustrate how such a system may work. A DMIA system, prior to the first consultation, could aid in the essential PC tasks of case finding/screening, diagnosing and, if needed, timely referral to specialists or urgent care. Similarly, for follow up consultations, a DMIA system can aid with the control/monitoring of these conditions, help check for additional health issues and for updating the PC provider about visits to other providers or further testing. The successful implementation of a DMIA system for these aspects of the PC consultation would improve the quality of the data obtained, which means earlier diagnosis and treatment; would improve the use of face-to-face consultation time, streamlining the interaction and allowing the focus to be the patient's needs, which ultimately would lead to better health outcomes and patient satisfaction. However, in order for such a system to be successfully incorporated, there are important considerations to be taken into account, such as the language to be used and the challenges for implementing eHealth innovations in primary care or healthcare in general. Given the benefits explored here, we foresee that DMIA systems could have an important impact in the PC consultation for diabetes and depression and, potentially, for other chronic conditions. Earlier case finding and a more accurate diagnosis, due to more and better-quality data, paired with improved monitoring of disease progress, should improve the quality of care and keep the management of chronic conditions at the primary care level. A somewhat simple, easily scalable technology could go a long way to improve the health of the millions of people affected with chronic conditions, especially if working in conjunction with already established health technologies such as EMRs and CDSS.
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