Lien entre durée de séjour et réadmissions en chirurgie et en obstétrique: étude de deux procédures à partir des données du PMSI = Using administrative data to assess the impact of length of stay on readmissions: study of two procedures in surgery and obstetrics

Position du problème: La mise en place de la tarification à l'activité pour les hôpitaux de court séjour pourrait entraîner une diminution des durées de séjour pour raisons financières. L'impact potentiel de ce phénomène sur la qualité des soins n'est pas connu. Les réadmissions identifiées à l'aide des données administratives hospitalières sont, pour certaines situations cliniques, des indicateurs de qualité des soins valides. Méthode: Étude rétrospective du lien entre la durée de séjour et la survenue de réadmissions imprévues liées au séjour initial, pour les cholécystectomies simples et les accouchements par voie basse sans complication, à partir des données du programme de médicalisation des systèmes d'information de l'Assistance publique-Hôpitaux de Paris des années 2002 à 2005. Résultats: Pour les deux procédures, la probabilité de réadmission suit une courbe en " J ". Après ajustement sur l'âge, le sexe, les comorbidités associées, l'hôpital et l'année d'admission, la probabilité de réadmission est plus élevée pour les durées de séjour les plus courtes : pour les cholécystectomies, odds ratio : 6,03 [IC95 % : 2,67-13,59] pour les hospitalisations d'un jour versus trois jours ; pour les accouchements, odds ratio : 1,74 [IC95 % : 1,05-2,91] pour les hospitalisations de deux jours versus trois jours. Conclusion: Pour deux pathologies communes, les durées de séjour les plus courtes sont associées à des probabilités de réadmission plus élevées. L'utilisation routinière des données du programme de médicalisation des systèmes d'information peut permettre d'assurer le suivi de la relation entre la réduction de la durée de séjour et les réadmissions. The prospective payment system for the French short-stay hospitals creates a financial incentive to reduce length of stay. The potential impact of the resulting decrease in length of stay on the quality of healthcare is unknown. Readmission rates are valid outcome indicators for some clinical procedures. Methods: Retrospective study of the association between length of stay and unplanned readmissions related to the initial stay, for two procedures: cholecystectomy and vaginal delivery. Data: Administrative diagnosis-related groups database of "Assistance publique-Hopitaux de Paris", a large teaching hospital, for years 2002 to 2005. Results: The risk of readmission according to length of stay, taking age, sex, comorbidity, hospital and year of admission into account, followed a J-shaped curve for both procedures. The probability of readmission was higher for very short stays, with odds ratios and 95% confidence intervals of 6.03 [2.67-13.59] for cholecystectomies (1- versus 3-night stays), and of 1.74 [1.05-2.91] for vaginal deliveries (2- versus 3-night stays). Conclusion: For both procedures, the shortest lengths of stay are associated with a higher readmission probability. Suitable indicators derived from administrative databases would enable monitoring of the association between length of stay and readmissions.

Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries.

With advances in the effectiveness of treatment and disease management, the contribution of chronic comorbid diseases (comorbidities) found within the Charlson comorbidity index to mortality is likely to have changed since development of the index in 1984. The authors reevaluated the Charlson index and reassigned weights to each condition by identifying and following patients to observe mortality within 1 year after hospital discharge. They applied the updated index and weights to hospital discharge data from 6 countries and tested for their ability to predict in-hospital mortality. Compared with the original Charlson weights, weights generated from the Calgary, Alberta, Canada, data (2004) were 0 for 5 comorbidities, decreased for 3 comorbidities, increased for 4 comorbidities, and did not change for 5 comorbidities. The C statistics for discriminating in-hospital mortality between the new score generated from the 12 comorbidities and the Charlson score were 0.825 (new) and 0.808 (old), respectively, in Australian data (2008), 0.828 and 0.825 in Canadian data (2008), 0.878 and 0.882 in French data (2004), 0.727 and 0.723 in Japanese data (2008), 0.831 and 0.836 in New Zealand data (2008), and 0.869 and 0.876 in Swiss data (2008). The updated index of 12 comorbidities showed good-to-excellent discrimination in predicting in-hospital mortality in data from 6 countries and may be more appropriate for use with more recent administrative data.

Adaptation au codage CIM-10 de 15 indicateurs de la sécurité des patients proposés par l'Agence étasunienne pour la recherche et la qualité des soins de santé (AHRQ) [ICD-10 adaptation of 15 Agency for Healthcare Research and Quality patient safety indicators].

BACKGROUND: In the United States, the Agency for Healthcare Research and Quality (AHRQ) has developed 20 Patient Safety Indicators (PSIs) to measure the occurrence of hospital adverse events from medico-administrative data coded according to the ninth revision of the international classification of disease (ICD-9-CM). The adaptation of these PSIs to the WHO version of ICD-10 was carried out by an international consortium. METHODS: Two independent teams transcoded ICD-9-CM diagnosis codes proposed by the AHRQ into ICD-10-WHO. Using a Delphi process, experts from six countries evaluated each code independently, stating whether it was "included", "excluded" or "uncertain". During a two-day meeting, the experts then discussed the codes that had not obtained a consensus, and the additional codes proposed. RESULTS: Fifteen PSIs were adapted. Among the 2569 proposed diagnosis codes, 1775 were unanimously adopted straightaway. The 794 remaining codes and 2541 additional codes were discussed. Three documents were prepared: (1) a list of ICD-10-WHO codes for the 15 adapted PSIs; (2) recommendations to the AHRQ for the improvement of the nosological frame and the coding of PSI with ICD-9-CM; (3) recommendations to the WHO to improve ICD-10. CONCLUSIONS: This work allows international comparisons of PSIs among the countries using ICD-10. Nevertheless, these PSIs must still be evaluated further before being broadly used.

Le financement des hôpitaux suisses par APDRGs

Dans le cadre du projet de modification du financement des hôpitaux, les partenaires de deux des plus grands cantons suisses - Vaud (Lausanne) et Zürich - ont décidé d'expérimenter le mode de système de paiement par cas traité dès le mois de janvier 2002, en utilisant la classification "All patient diagnosis related groups" (APDRGs). Pourquoi a t-on choisi les APDRGs? Ce système de classification des patients est-il adapté au contexte helvétique? Quelles en sont les caractéristiques? Quelles précautions doit-on prendre pour limiter les risques attendus? Quelles adaptations implique ce nouveau financement pour les différents acteurs?

Financement des hôpitaux par APDRG et compensation des risques entre assureurs

Ce rapport analyse les données (coût par patient et par assureur) du CHUV (Centre hospitalier universitaire vaudois) pour 2002 et 2003 et conclue que la facturation en APDRG n'a pas n'effet de désolidarisation entre les assureurs.

Adaptation du "GROUPER" aux statistiques médicales VESKA (1980-1986)

Le présent cahier qui termine une série consacrée à l'adaptation du "grouper" aux statistiques médicales VESKA des années 1980-1986, a pour but de donner une vue d'ensemble des procédures nécessaires à cette adaptation.

Groupes diagnostiques utilisés sur SIMULIT 13

La nomenclature des diagnostics est celle de la Classification internationale des maladies (9e révision), utilisée par la Statistique médicale VESKA depuis 1980. Les trois premiers chiffres du code ont été utilisés; seul le premier diagnostic a été retenu.

Effects of age, birth cohort and period of death on Swiss cancer mortality, 1951-1984.

Swiss death certification data over the period 1951-1984 for total cancer mortality and 30 major cancer sites in the population aged 25 to 74 years were analysed using a log-linear Poisson model with arbitrary constraints on the parameters to isolate the effects of birth cohort, calendar period of death and age. The overall pattern of total cancer mortality in males was stable for period values and showed some moderate decreases in cohort values restricted to the generations born after 1930. Cancer mortality trends were more favourable in females, with steady, though moderate, declines in both cohort and period values. According to the estimates from the model, the worst affected generation for male lung cancer was that born around 1910, and a flattening of trends or some moderate decline was observed for more recent cohorts, although this decline was considerably more limited than in other European countries. There were decreases in cohort and period values for stomach, intestine and oesophageal cancer in both sexes and (cervix) uteri in females. Increases were observed in both cohort and period trends for pancreas and liver in males and for several other neoplasms, including prostate, brain, leukaemias and lymphomas, restricted, however, for the latter sites, to the earlier cohorts and hence partly attributable to improved diagnosis and certification in the elderly. Although age values for lung cancer in females were around 10-times lower than in males, upward trends in female lung cancer cohort values were observed in subsequent cohorts and for period values from the late 1960's onwards. Therefore, future trends in female lung cancer mortality should continue to be monitored. The application of these age/period/cohort models thus provides a summary guide for the reading and interpretation of cancer mortality trends, although it cannot replace careful inspection of single age-specific rates.

Concurrence et régulation dans le secteur hospitalier

Pour mettre en évidence le rôle respectif de la concurrence et de la régulation, cet article traitera essentiellement de la question du financement des hôpitaux. Après une section 1 consacrée aux justifications de la régulation, les modalités de celles-ci seront analysées dans la section 2 avant d'étudier la place de la concurrence dans la section 3. [Auteur, p. 62] [Table des matières] 1. Les fondements de la régulation du système de santé. 1A. Pourquoi réguler (assurance-maladie ; la production de soins). 1B. Comment réguler. - 2. La régulation des tarifs hospitaliers. 2A. Principes généraux de paiement. 2B. La tarification à l'activité. 3. Concurrence et régulation. 3A. Concurrence fictive, spécialisations, et concurrence privé-public. 3B. La concurrence par la qualité. 3C. La concurrence en prix.

DRGs in hospital management.

One of the traditional tasks of district and hospital managers has been to attempt to explain variations in average length of stay, average cost per day and average cost per case, between different hospitals. The need for such explanations has become more acute as a result of the recent emphasis on 'performance indicators' as measures of the efficiency of hospitals. The task of explaining these differences has not been rendered easier by the lack of appropriate management information for this purpose.

Measuring potentially avoidable hospital readmissions.

The objectives of this study were to develop a computerized method to screen for potentially avoidable hospital readmissions using routinely collected data and a prediction model to adjust rates for case mix. We studied hospital information system data of a random sample of 3,474 inpatients discharged alive in 1997 from a university hospital and medical records of those (1,115) readmitted within 1 year. The gold standard was set on the basis of the hospital data and medical records: all readmissions were classified as foreseen readmissions, unforeseen readmissions for a new affection, or unforeseen readmissions for a previously known affection. The latter category was submitted to a systematic medical record review to identify the main cause of readmission. Potentially avoidable readmissions were defined as a subgroup of unforeseen readmissions for a previously known affection occurring within an appropriate interval, set to maximize the chance of detecting avoidable readmissions. The computerized screening algorithm was strictly based on routine statistics: diagnosis and procedures coding and admission mode. The prediction was based on a Poisson regression model. There were 454 (13.1%) unforeseen readmissions for a previously known affection within 1 year. Fifty-nine readmissions (1.7%) were judged avoidable, most of them occurring within 1 month, which was the interval used to define potentially avoidable readmissions (n = 174, 5.0%). The intra-sample sensitivity and specificity of the screening algorithm both reached approximately 96%. Higher risk for potentially avoidable readmission was associated with previous hospitalizations, high comorbidity index, and long length of stay; lower risk was associated with surgery and delivery. The model offers satisfactory predictive performance and a good medical plausibility. The proposed measure could be used as an indicator of inpatient care outcome. However, the instrument should be validated using other sets of data from various hospitals.