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Record W2520911970 · doi:10.1002/wmh3.199

Medical Errors: Next Steps

2016· article· en· W2520911970 on OpenAlexaboutno aff
Arnauld Nicogossian, Bonnie Stabile, Otmar Kloiber, Thomas Zimmerman

Bibliographic record

VenueWorld Medical & Health Policy · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careMedicinePsychological interventionPopulationEpidemiologyFamily medicineAdverse effectGerontologyDemographyEnvironmental healthPolitical sciencePsychiatrySociology

Abstract

fetched live from OpenAlex

Two decades of advances in genomics, information technology, and precision medicine hold the promise for better care and improved survival for patients with chronic disorders. Patients expect that the health-care system, especially in countries with a market economy, will continue to offer solutions and cures to many illnesses. Yet, concerns over morbidity and mortality from unsafe health-care practices continue to linger and erode patient confidence. The Institute of Medicine of the U.S. National Academies sounded the alarm on patient safety in a report published 17 years ago and called for an examination of health care practices (Institute of Medicine, 2000). Since then several epidemiological studies have been conducted to determine the extent and causes of, and interventions for, adverse medical events and patient safety (Jha et al., 2013; Kemp, Santana, Southern, McCormack, & Quan, 2016; O'Hagan, MacKinnon, Persaud, & Etchegary, 2009). A survey from Australia, Canada, Germany, the Netherlands, New Zealand, the United Kingdom, and the United States estimated 12 to 20 percent adverse events, with disability more common than mortality, and a higher disability-adjusted life year (DALY) in developing countries (O'Hagan et al., 2009). The authors estimate that seven types of adverse events considered in this study constitute the 20th leading cause of morbidity and mortality for the world's population. In the United States, medical errors and adverse effects (Grober & Bohenen, 2005; Makary & Daniel, 2016) continue to be at the center of controversy and are the subject of continued news media headlines. One in seven U.S. Medicare1 patients experiences a medical error (Agency for Healthcare Research and Quality, 2014). Prescription drugs are reported for nearly 100,000 hospitalizations each year. Many in-patient health institutions (hospitals) are increasingly employing physician hospitalists to care for the admissions. Transitions in care, from one physician to another, or to a hospitalist, can lead to preventable harm related to medications (Graham, Scudder, & Stokowski, 2015; Velo & Minuz, 2009). Many countries and international organizations such as the World Health Organization and the World Medical Association (53rd World Medical Association General Assembly, 2002) have published guidelines to improve patient safety. Most countries with a market economy have established registries for reporting medication adverse events. New tools such as health forecasting (Soyiri & Reidpath, 2013) can assist with better epidemiological data collection and research. Medical errors should be recognized as a standalone diagnostic code in the International Statistical Classification of Diseases and Related Health Problems (ICD) to facilitate the collection of global information on morbidity and mortality and DALY estimates. The global health-care enterprise is diverse, and one of the 20 largest industries financed by governments and private entities. It is estimated that by 2022, health expenditures for the developing (about 33 percent of the share) and market economy countries (about 67 percent) will exceed 12 trillion USD. The world population is growing and aging. The number of health-care professionals is not in step with future population needs. Health-care facilities are expected to help improve health outcomes. Preventable errors are costly, especially in human suffering, and should be addressed expediently. Many health-care providers have already adopted patient safety safeguards and standards. Many safety practices are simple and should be adopted as soon as possible. Increasing the number of providers, reducing working hours and fatigue, improving communications, providing systems for blame-free reporting, and engaging patients in understanding the health safety culture should be priorities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.075
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.070
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0050.002
Science and technology studies0.0080.009
Scholarly communication0.0160.030
Open science0.0060.014
Research integrity0.0250.032
Insufficient payload (model declined to judge)0.0750.028

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.108
GPT teacher head0.514
Teacher spread0.406 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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