Electronic health records contributing to physician burnout

Collier, Roger

doi:10.1503/cmaj.109-5522

Cited by 78 publications

(49 citation statements)

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“…30,31 Machine learning and natural language processing methods extract structured information from unstructured text data, 15 which could potentially remove some of the EHR-associated burden from clinicians. 6,32,33 These examples predominantly come from referral care settings, not from primary care, where the spectrum of illness is wider, and clinicians have fewer diagnostic instruments or tests available. Despite optimism for using AI to benefit primary care, there is no comprehensive review of what contribution AI has made so far, and thus little guidance on how best to proceed with research.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Primary Care Research: A Scoping Review

et al. 2020

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PURPOSE Rapid increases in technology and data motivate the application of artificial intelligence (AI) to primary care, but no comprehensive review exists to guide these efforts. Our objective was to assess the nature and extent of the body of research on AI for primary care. METHODSWe performed a scoping review, searching 11 published or gray literature databases with terms pertaining to AI (eg, machine learning, bayes* network) and primary care (eg, general pract*, nurse). We performed title and abstract and then full-text screening using Covidence. Studies had to involve research, include both AI and primary care, and be published in English. We extracted data and summarized studies by 7 attributes: purpose(s); author appointment(s); primary care function(s); intended end user(s); health condition(s); geographic location of data source; and AI subfield(s). RESULTSOf 5,515 unique documents, 405 met eligibility criteria. The body of research focused on developing or modifying AI methods (66.7%) to support physician diagnostic or treatment recommendations (36.5% and 13.8%), for chronic conditions, using data from higher-income countries. Few studies (14.1%) had even a single author with a primary care appointment. The predominant AI subfields were supervised machine learning (40.0%) and expert systems (22.2%).CONCLUSIONS Research on AI for primary care is at an early stage of maturity. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed. Ann Fam Med 2020;18:250-258. https://doi.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Primary Care Research: A Scoping Review

et al. 2020

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“…With the push towards meeting a better quality of patient experience whilst simultaneously reducing costs, the strategies used to prioritize patient care may cause additional pressures on staff which also contribute to healthcare worker burnout (Bodenheimer & Sinsky, ). For example, clerical load has been cited as a leading cause of healthcare worker burnout (Collier, ) and these excessive administrative duties affect the ability to prioritize patient care (Bodenheimer & Sinsky, ). Healthcare workers recognize that the quality of care they deliver is compromised by such factors (Bodenheimer & Sinsky, ) and have recently called for an additional priority: the protection of workers.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking working conditions for health and safety in the frontline healthcare industry: Perspectives from Australia and Malaysia

McLinton

Loh

Dollard

et al. 2018

Journal of Advanced Nursing

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“…20,21 Moreover, much of the current evidence in diabetes is, in fact, much stronger than any link to improved outcomes associated with general health information technologies, such as electronic health records, which inexplicably require little or no return on investment, and appear to be driving physician burnout. [22][23][24] A Digital Model of Diabetes Care If patients do use an app in their own care, it is often done independently of their care provider, who cannot deal with the rapid pace of the development of apps and the extra data points they can generate. Clinic visits are increasingly short and cannot accommodate the multitude of disparate data sources and the rapidly developing app ecosystem.…”

Section: Evidence and Excusesmentioning

confidence: 99%

A Digital-First Model of Diabetes Care

Cafazzo

2019

Diabetes Technology & Therapeutics

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If we were to create the diabetes care experience anew, there is little doubt that it would not resemble the current bricks-and-mortar way we do things currently. For however a future model of care is designed, it would assume a digital-first approach, whereby the modern conveniences of digitally-mediated services we have experienced in other industries would be reflected in our diabetes care. To this end, our diabetes data would be liberated, transparent to those that need it, but safe and secure otherwise. We would have access to new tools that create insights that lower the burden, not add to it. And access to care would be just in time, convenient, and from a distance when needed. What is stopping a digital-first model is complex and deeply seated, but not insurmountable with engagement from industry, regulators, and care providers that are all willing to modernize the way care is delivered. Personal human interaction will continue to play an important part in the care for millions of people living with diabetes, no matter the sophistication of these digital services. What these technologies will provide is the human capacity to deal with the higher need, vulnerable people for whom access to timely care is an issue. Moreover, it will provide choice for an increasingly diverse population that seeks options for the form, and the delivery, of their personalized care.

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Electronic health records contributing to physician burnout

Cited by 78 publications

References 0 publications

Artificial Intelligence and Primary Care Research: A Scoping Review

Artificial Intelligence and Primary Care Research: A Scoping Review

Benchmarking working conditions for health and safety in the frontline healthcare industry: Perspectives from Australia and Malaysia

A Digital-First Model of Diabetes Care

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