Deskilling and adaptation among primary care physicians using two work innovations

Hoff, Timothy

doi:10.1097/hmr.0b013e31821826a1

Cited by 78 publications

(42 citation statements)

References 28 publications

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“…Note that computer-based systems, such as MDSS, are often seen by clinicians as an extra burden in their day-to-day practice [18]. The problem may appear when the MDSS conflicts with guidelines of medical practice [19], something bound to happen unless those guidelines are somehow fed as prior knowledge to the intelligent systems. In this scenario, interpretability might be seen as an opportunity to make model performance and compliance with guidelines compatible goals.…”

Section: Interpretability and Explainabilitymentioning

confidence: 99%

Societal Issues Concerning the Application of Artificial Intelligence in Medicine

Vellido

2018

Kidney Dis

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Background: Medicine is becoming an increasingly data-centred discipline and, beyond classical statistical approaches, artificial intelligence (AI) and, in particular, machine learning (ML) are attracting much interest for the analysis of medical data. It has been argued that AI is experiencing a fast process of commodification. This characterization correctly reflects the current process of industrialization of AI and its reach into society. Therefore, societal issues related to the use of AI and ML should not be ignored any longer and certainly not in the medical domain. These societal issues may take many forms, but they all entail the design of models from a human-centred perspective, incorporating human-relevant requirements and constraints. In this brief paper, we discuss a number of specific issues affecting the use of AI and ML in medicine, such as fairness, privacy and anonymity, explainability and interpretability, but also some broader societal issues, such as ethics and legislation. We reckon that all of these are relevant aspects to consider in order to achieve the objective of fostering acceptance of AI- and ML-based technologies, as well as to comply with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. Our specific goal here is to reflect on how all these topics affect medical applications of AI and ML. This paper includes some of the contents of the “2nd Meeting of Science and Dialysis: Artificial Intelligence,” organized in the Bellvitge University Hospital, Barcelona, Spain. Summary and Key Messages: AI and ML are attracting much interest from the medical community as key approaches to knowledge extraction from data. These approaches are increasingly colonizing ambits of social impact, such as medicine and healthcare. Issues of social relevance with an impact on medicine and healthcare include (although they are not limited to) fairness, explainability, privacy, ethics and legislation.

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Section: Interpretability and Explainabilitymentioning

confidence: 99%

Societal Issues Concerning the Application of Artificial Intelligence in Medicine

Vellido

2018

Kidney Dis

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“…Hoff 45 conducted interviews with primary care physicians in the USA to examine implementation of two innovations, ie, electronic health record use and evidence-based practice guidelines, and found that primary care physicians use these innovations in ways that indicate "deskilling outcomes," including stereotyping of patients and decreasing confidence in clinical decision-making. The pressure to increase patient volume and comply with complex requirements of chronic disease guidelines left several physicians feeling unable to provide the type of holistic patient care that leveraged their full range of clinical knowledge and skills.…”

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confidence: 99%

Innovation implementation in the context of hospital QI: lessons learned and strategies for success

Rangachari¹

2018

IEH

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Abstract:In 1999, the Institute of Medicine (IOM) reported that 98,000 people die each year due to medical errors. In the following years, the focus on hospital quality was intensified nationally, with policymakers providing evidence-based practice guidelines for improving health care quality. However, these innovations (evidence-based guidelines) that were being produced at policy levels were not translating to clinical practice at the hospital organizational level easily, and stark variations continued to persist, in the quality of health care. Circa 2009, nearly a decade after the release of the IOM report, the health care organizational literature began referring to this challenge as "innovation implementation failure" in health care organizations (HCOs), ie, failure to implement an evidence-based practice that is new to a HCO. This stream of literature drew upon management research to explain why innovation implementation failure occurs in HCOs and what could be done to prevent it. This paper conducts an integrative review of the literature on "innovation implementation" in hospitals and health systems over the last decade, since the spotlight was cast on "innovation implementation failure" in HCOs. The review reveals that while some studies have retrospectively sought to identify the key drivers of innovation implementation, through surveys and interviews of practitioners (the "what"), other studies have prospectively sought to understand how innovation implementation occurs in hospitals and health systems (the "how"). Both make distinctive contributions to identifying strategies for success in innovation implementation. While retrospective studies have helped identify the key drivers of innovation implementation, prospective studies have shed light on how these drivers could be attained, thereby helping to develop context-sensitive management strategies for success. The literature has called for more prospective research on the implementation and sustainability of health care innovations. This paper summarizes the lessons learned from the literature, discusses the relevance of management research on innovation implementation in HCOs, and identifies future research avenues.

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“…As we offload more and more work to machines, we might also need to take care that we "don't let the machine take our work", and most importantly, don't surrender ourselves to the developing intelligence of high technology. Meanwhile, we should be aware that our concerns about technology have little to do with technology itself, but rather with the way we design and use them in practice (Bailey, 2011;Hoff, 2011). Doctors should take the agency on when, how and why to use technology and not allow technology take it away from them.…”

Section: Resultsmentioning

confidence: 99%

“…Hoff also found that physicians increasingly lost their ability to understand and abstract the richness and uniqueness of patients' information given the standardized EHR format, consequently undercutting their "ability to make informed decisions around diagnosis and treatment" (Hoff, 2011) Both doctor-patient communication and physical examination skills have been and remain important venues for obtaining information critical for making correct diagnoses. Using them together is sufficient for making good diagnoses.…”

Section: Concerns On the Negative Effects Of Medical Technology On Hementioning

confidence: 99%

Will Medical Technology Deskill Doctors?

2016

IES

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This paper discusses the impact of medical technology on health care in light of the fact that doctors are becoming more reliant on technology for obtaining patient information, making diagnoses and in carrying out treatments. Evidence has shown that technology can negatively affect doctor-patient communications, physical examination skills, and development of clinical knowledge. We propose three possible approaches to medical education and professional development to assist human doctors in acquiring a better understanding of the relative role of technology. The approaches include providing undergraduate and professional development training on using medical technology, helping students recognize the importance of clinical evidence, and helping doctors establish autonomy while interfacing with medical technology.

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Deskilling and adaptation among primary care physicians using two work innovations

Cited by 78 publications

References 28 publications

Societal Issues Concerning the Application of Artificial Intelligence in Medicine

Societal Issues Concerning the Application of Artificial Intelligence in Medicine

Innovation implementation in the context of hospital QI: lessons learned and strategies for success

Will Medical Technology Deskill Doctors?

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