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Enregistrement W35444332 · doi:10.1016/j.anbehav.2020.10.011

Transforming the Culture Of Patient Safety in Organizations

2010· article· en· W35444332 sur OpenAlexfundno aff
Matthew Grissinger

Notice bibliographique

RevueP & T · 2010
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueOrganizational Change and Leadership
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute on Deafness and Other Communication DisordersNatural Sciences and Engineering Research Council of CanadaUniversity of Pennsylvania
Mots-clésMindsetHarmSafety cultureNear missOrganizational cultureInstitutionPublic relationsPatient safetyPsychologyAffect (linguistics)Health careWork (physics)Social psychologyBusinessPolitical scienceComputer scienceLawEngineeringManagement

Résumé

récupéré en direct d'OpenAlex

During the past decade, those most involved in the patient safety movement have come to realize that preventing catastrophic events—or any avoidable harm to patients, for that matter—requires more than simply changing systems and implementing best practices. Also required are changes in our mindset about patient safety and adjustments in the underlying “culture” in which health care is provided, assessed, and improved. One of the most significant predictors of success in keeping patients safe is the state of an organization’s culture.1 For instance, we might ask the following questions: How open are employees to reporting risk, near misses, and errors? Do they regard near misses as system failures that reveal potential danger or as evidence of success because potential harm was avoided? How receptive are an organization’s leaders and executives to hearing about problems that affect patient safety? Is it just by chance that more serious mistakes don’t happen? The answers to these questions about an institution’s culture do not rest within a single individual. The entire organization, from board members to first-line workers, should be surveyed to establish a baseline that describes the current culture upon which strategies for improvement can be determined and upon which repeated measurements can show change.1 An institution’s culture can be found in the pattern of shared basic assumptions about the values, beliefs, and behaviors that have been transmitted to the work-force in both explicit and implicit ways.2 Thus, the culture encompasses the observable customs, behavioral norms, stories, and rituals that take place as well as the unobservable assumptions, values, beliefs, and ideas shared by groups. The most common means of measuring an organization’s culture is to survey an adequate sample of employees. Two examples of validated survey tools can be found on the Web sites of the Agency for Healthcare Research and Quality (www.ahrq.gov/qual/patientsafetyculture) and the Health Research and Education Trust (www.hret.org/hret/programs/saq.htm). In addition to staff surveys, other ways to add to the pool of knowledge consist of the following steps: Patient surveys. Surveys can be used to determine the perceptions that patients and the community have about an organization’s culture. New-hire interviews. During the first few months, supervisors might ask new employees these questions: Are we doing things differently from what we mentioned in your earlier interview? When you first joined the organization, what details struck you? Is there anything that might make you want to leave your position? A new worker’s answers to these questions can help pinpoint underlying problems in the organization’s culture, particularly those that might conflict with the organization’s espoused values. Rounding. Rounds that cover patient safety can be used as a forum to learn more about an organization’s culture, particularly if standard queries are included in the script used for interacting with staff and patients. Focus groups. Small groups can meet to discuss the organization’s progress in improving patient safety. The organization’s leaders can gather clues about domains that should be targeted by asking employees this question: What would you tell new colleagues about what they need to know to get their job done? Exit interviews. Knowing rates of staff turnover and accessing information gleaned during employees’ exit interviews can help uncover undesirable elements that exist in an organization’s culture. Performance evaluations. Aggregate data can be gathered about domains used to measure the performance of all personnel, such as teamwork, communication, respect, and transparency. The combined information obtained should be used to perform a gap analysis. This type of study could be conducted to compare the current culture with the ideal culture to identify opportunities for improvement. Comparing data from various large groups can also provide important information. Comparisons can be made, for example, between the perceptions of (1) board members and the executive team, (2) the executive team and management, (3) management and staff, (4) new staff members and experienced staff, (5) nurses and pharmacists, and (6) patients and health care practitioners. Differences in perceptions among specific groups can illuminate problems that might not be detected by an analysis of the aggregate data alone. Findings should be shared with all employees, and the meaning of the results should be clearly explained, either in a meeting or in writing. Feedback about how to best support patient safety should be sought throughout the organization. The more feedback that is requested and acted upon, the faster change can occur.1 As an added bonus, the surveying process itself can help an organization articulate the most important aspects of the desired culture, and the actions taken in response to the survey can help to establish the organization’s commitment to a culture of safety. Data should also be used to demonstrate improvements in key patient safety domains over time. However, the same tools should be used to measure the same groups so that internal comparisons are valid. New measures can be introduced as desired, but a core group of key measures should remain unchanged. Laying a foundation for a culture of safety takes time and commitment, but simply performing a survey is no indication of an organization’s progress toward improving safety. The organization may have an event-reporting program, a reasonably just culture, and a fair amount of assessment activity and teamwork, and it may have succeeded in implementing some important safety measures. However, as James Reason notes, “assembling the parts of a machine is not the same thing as making it work.”3 It is not enough to possess some of the elements of a culture of safety; we need to totally transform the way we perceive and react to risks that threaten the safety of patients. Measuring the culture in an organization builds the groundwork for this transformation.4

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,028
score de la tête « metaresearch » (Gemma)0,047
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,028
Score d'incertitude au seuil0,149

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0280,047
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0100,030
Communication savante0,0160,010
Science ouverte0,0020,022
Intégrité de la recherche0,0040,013
Charge utile insuffisante (le modèle a refusé de juger)0,0130,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,198
Écart entre enseignants0,188 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2010
Routes d'admission1
Résumé présentoui

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