This paper examines the inconsistencies or distortions among three medical realities: patients’ physical reality (as reflected in clinical observations, lab reports, and other “objective” measures); ...clinicians’ mental models of patients’ conditions; and how that information is represented in the patient’s electronic chart—the electronic health record (EHR). We created a typology based on the semiotic triangle of “symbol,” “thought or reference,” and “referent.” Differing perspectives (or realities) are illustrated with examples from our observations in hospitals and medical facilities, interviews with clinicians, IT personnel and IT vendors, computer logs, and error reports. Scenarios/models enumerate how the differing perspectives can misalign to produce distortions in comprehension and treatment. These are categorized according to an emergent typology derived from the cases themselves and refined based on insights gained from the literature on interactive sociotechnical systems analysis, decision support science, and human-computer interaction. The scenarios reflect the misalignment between patients’ physical realities, clinicians’ mental models, and EHRs, identifying five types of misrepresentation: IT data too narrowly focused; IT data too broadly focused; EHRs miss critical reality; data multiplicities—perhaps contradictory or confusing; distortions from data reflected back and forth across users, sensors, and others. Conclusion: With humans, there is a physical reality and actors’ mental models of that reality. In healthcare, there is another player: the EHR/healthcare IT, which implicitly and explicitly reflects many mental models, facets of reality, and measures thereof that vary in reliability and consistency. EHRs are both microcosms and shapers of medical care.
Professor Naoki Ikegami's "Fee-for-service payment - an evil practice that must be stamped out" summarizes many of the failings of alternatives to fee-for-service (FFS) payment systems. His article ...also offers several suggestions for improving FFS systems. However, even powerful arguments against many of the alternatives to FFS, does not make a convincing argument for FFS systems. In addition, there are significant misunderstandings in Professor Ikegami's presentation of and use of United States payment methods, the role of private vs. public insurance systems, and the increasing role of "accountable care organizations.
The COVID-19 pandemic response in the United States has exposed significant gaps in information systems and processes that prevent timely clinical and public health decision-making. Specifically, the ...use of informatics to mitigate the spread of SARS-CoV-2, support COVID-19 care delivery, and accelerate knowledge discovery bring to the forefront issues of privacy, surveillance, limits of state powers, and interoperability between public health and clinical information systems. Using a consensus-building process, we critically analyze informatics-related ethical issues in light of the pandemic across 3 themes: (1) public health reporting and data sharing, (2) contact tracing and tracking, and (3) clinical scoring tools for critical care. We provide context and rationale for ethical considerations and recommendations that are actionable during the pandemic and conclude with recommendations calling for longer-term, broader change (beyond the pandemic) for public health organization and policy reform.
Nonvalvular atrial fibrillation (NVAF) affects almost 6 million Americans and is a major contributor to stroke but is significantly undiagnosed and undertreated despite explicit guidelines for oral ...anticoagulation.
The aim of this study is to investigate whether the use of semisupervised natural language processing (NLP) of electronic health record's (EHR) free-text information combined with structured EHR data improves NVAF discovery and treatment and perhaps offers a method to prevent thousands of deaths and save billions of dollars.
We abstracted 96,681 participants from the University of Buffalo faculty practice's EHR. NLP was used to index the notes and compare the ability to identify NVAF, congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke or transient ischemic attack, vascular disease, age 65 to 74 years, sex category (CHA
DS
-VASc), and Hypertension, Abnormal liver/renal function, Stroke history, Bleeding history or predisposition, Labile INR, Elderly, Drug/alcohol usage (HAS-BLED) scores using unstructured data (International Classification of Diseases codes) versus structured and unstructured data from clinical notes. In addition, we analyzed data from 63,296,120 participants in the Optum and Truven databases to determine the NVAF frequency, rates of CHA
DS
‑VASc ≥2, and no contraindications to oral anticoagulants, rates of stroke and death in the untreated population, and first year's costs after stroke.
The structured-plus-unstructured method would have identified 3,976,056 additional true NVAF cases (P<.001) and improved sensitivity for CHA
DS
-VASc and HAS-BLED scores compared with the structured data alone (P=.002 and P<.001, respectively), causing a 32.1% improvement. For the United States, this method would prevent an estimated 176,537 strokes, save 10,575 lives, and save >US $13.5 billion.
Artificial intelligence-informed bio-surveillance combining NLP of free-text information with structured EHR data improves data completeness, prevents thousands of strokes, and saves lives and funds. This method is applicable to many disorders with profound public health consequences.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, ...however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.
The missing (1) To my mind, Drs Singh and Sittig fail to acknowledge the extent of our ignorance about errors, especially the most common errors involved with EHRs: medication prescribing errors and ...diagnostic errors. Because so much of their plan revolves around the idea that we can learn from better error reporting-even though so many errors are unknown-they build a framework on a vast void. ...theirs is a structure with many fine elements, but too much of it rests on what may not only be unknown but usually unknowable.
We review the extent and functionality of computerized physician order entry (CPOE) systems in seven Western countries. We compare nations' implementation levels; linkages with other health care ...information technologies; amount and types of use by clinicians; drivers of implementation; inclusion of decision-support systems and electronic medical records; and goals (for example, patient safety and efficiency). Implementation of CPOE is slower and more problematic than anticipated (adoption rates are 20 percent or less) and often poorly integrated, inducing new errors and generating frustration with user interfaces and repetitive tasks. Nevertheless, the advantages of CPOE remain compelling.
Celotno besedilo
Dostopno za:
CEKLJ, DOBA, IZUM, KILJ, NUK, ODKLJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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