MétaCan
Menu
Retour à la cohorte
Enregistrement W1762750345 · doi:10.1002/jhm.2349

Making progress with code status documentation

2015· letter· en· W1762750345 sur OpenAlexaboutno aff
Rashmi Sharma, Anthony C. Breu

Notice bibliographique

RevueJournal of Hospital Medicine · 2015
Typeletter
Langueen
DomaineMedicine
ThématiquePalliative Care and End-of-Life Issues
Établissements canadiensnon disponible
Organismes subventionnairesEunice Kennedy Shriver National Institute of Child Health and Human Development
Mots-clésMedicineDocumentationHospital medicineCode (set theory)MEDLINEMedical physicsFamily medicineProgramming languageComputer science

Résumé

récupéré en direct d'OpenAlex

In the hospital, cardiopulmonary resuscitation (CPR) is the default treatment for a patient who suffers a cardiac arrest. Clinician assessment of patient preferences regarding resuscitation, with appropriate documentation in the medical record, is therefore essential for patients who do not wish to be resuscitated.1 In addition, given frequent patient handoffs between physicians, consistent documentation of patient preferences is critical.2 Unfortunately, multiple deficiencies in the quality of code status documentation have been identified in prior work.3,4 In this issue of the Journal of Hospital Medicine, Weinerman and colleagues5 build on this literature by not only evaluating the completeness of code status documentation in multiple documentation sites, but also its consistency. In this Canadian multihospital study, the authors found that only 38 of the 187 patients (20%) admitted to 1 of 4 medicine services had complete and consistent documentation of code status. Even more worrisome is that two-thirds of the patients had inconsistent code status documentation. Although most of these inconsistencies involved missing information in 1 of the 5 sites of documentation (progress note, physician order, electronic resident sign-out lists, nursing-care plan, and do-not-resuscitate [DNR] face sheet), 31% were deemed clinically significant (eg, DNR in 1 source and full code in another). Such inconsistent documentation represents a serious threat to patient safety, and highlights the need for interventions aimed at improving the quality and reliability of code status documentation. The authors identified 71 cases where code status documentation in progress notes was missing or inconsistent with documentation in other sites. Sixty of these notes lacked mention of a preference for full code status, 10 lacked documentation of DNR status, and 1 note incorrectly documented full code rather than DNR status. Interpretation of these findings requires consensus on whether the progress note is an appropriate location for code status documentation. With the evolution of the electronic medical record, the role of the progress note has changed, and unfortunately, these notes have become a lengthy chronicle of a patient's hospital course that includes all clinical data, medical problems, and an array of bottom-of-the-list items such as code status. Information is easily added, but rarely removed, and what remains often goes unedited even for high-stakes issues such as code status. Given the potential for copying and pasting of progress notes day after day, it is critical that clinicians periodically review the code status documented in the patient's notes and update this information as those preferences change. One solution that may minimize the potential for inaccurate documentation in progress notes is for institutions to utilize a separate note for code status documentation that the clinician fills out following any code status discussion. Having this note clearly labeled (eg, Code Status Note) and in a universal place within the electronic record may provide a reliable and efficient way for both physicians and nurses to identify a patient's preferences, while minimizing the inclusion of repetitive information in daily notes. Furthermore, if entered into a discrete field within the electronic record, this information could then autopopulate other sites (eg, sign-out, nursing forms), thereby maintaining consistency. Use of note templates can provide a way to then help standardize the quality of information that is included in this type of code status note. An alternate solution that may minimize the potential for inaccurate implementation of code status preferences is to focus on the fact that they are orders. As this study highlights, there is a need to improve both the completeness and consistency of code status documentation and, to this end, orders such as the Medical Orders for Life-Sustaining Treatment (MOLST) or Physician Orders for Life-Sustaining Treatment (POLST) may help.6 Not only do these orders expand upon resuscitation preferences to include broader preferences for treatment in the context of serious illness, but they are also meant to serve as a standard way to document patient care preferences across healthcare settings. Although the MOLST and POLST primarily aim to translate patient preferences into medical orders to be followed outside of the hospital, their implementation into the electronic medical record may provide a more consistent way to document patient preferences in the hospital as well. Although many studies have identified the need to improve the quality of code status discussions,7–10 the work by Weinerman and colleagues reminds us that attention to documentation is also critical. Ensuring that the electronic medical record contains documentation of the patient's resuscitation preferences and overall goals of care, and that this information can be found easily and reliably by physicians and nurses, should drive future quality improvement and research in this area.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,330
Score d'incertitude au seuil0,760

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,110
Tête enseignante GPT0,443
Écart entre enseignants0,333 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations1
Publié2015
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Hospital MedicineMême sujetPalliative Care and End-of-Life IssuesTravaux en français237 207