Suchergebnisse

Dans un contexte d'amélioration de la performance des soins, les systèmes de santé ont développé des dispositifs de management centrés sur des indicateurs de qualité des soins et de sécurité des patientꞏeꞏs (IQS). Ces IQS constituent des outils de monitoring de la qualité des soins, d'évaluation des actions d'amélioration mises en oeuvre, et d'information des prestataires et des bénéficiaires des soins. Ils contribuent aussi au pilotage et à la régulation du système et des prestataires de soins. Le management basé sur ces IQS postule que la diffusion publique des indicateurs accroît la transparence et encourage l'amélioration de la qualité dans les établissements de santé. En Suisse, les IQS ont été mis en oeuvre dans les hôpitaux et les cliniques à partir de 2008. Leurs résultats, publiés annuellement par l'ANQ et l'OFSP, visent à « faire des comparaisons transparentes au niveau national. A partir de ces comparaisons, les hôpitaux et les cliniques peuvent développer des mesures ciblées pour améliorer leur qualité ». Ces indicateurs, dits « institutionnels », sont relayés au niveau local (départements, services, unités) par de nombreux indicateurs dits « cliniques » ou « locaux » destinés à l'évaluation interne et à soutenir des mesures d'amélioration des pratiques professionnelles. Ce passage de la politique publique à la pratique clinique ne va pas de soi. En témoigne le déplacement progressif de l'objet de la recherche sur la qualité des soins vers des problématiques d'organisation du travail. En effet, si les premiers travaux de recherche se sont concentrés sur la conception métrologique des indicateurs et l'interprétation de leurs résultats, les études récentes se sont intéressées à l'implémentation des indicateurs dans les établissements de santé. Elles ont ainsi montré l'importance des facteurs organisationnels transversaux tels que la diffusion de la culture, la mise à disposition de ressources techniques ou le leadership. Ces facteurs constituent une étape essentielle pour appréhender la dimension ...

IntroductionPostpartum hemorrhage (PPH) remains a major cause of morbidity and mortality worldwide. Geo-temporal comparisons of in-hospital PPH incidence remain a challenge due to differences in definition, data quality and the absence of accurate, validated indicators.
Objectives and ApproachTo compare the incidence of PPH using different definitions to assess the need for a validated indicator. Singleton births from 2014-2016 at Lausanne University Hospital, Switzerland, were included. PPH was defined based on 1) clinical diagnosis using International Classification of Diseases (ICD-10-GM) PPH diagnostic codes, 2) volume of blood loss ≥500ml for vaginal births and ≥1000ml for cesareans 3) peripartum Hb change >2g/dl in vaginal births and ≥4g/dl in cesareans and 4) fulfillment of criteria from definition one, two or three. Data were extracted from hospital discharge data and linked with electronic health records.
ResultsThere were 2529, 2660 and 2715 singleton births in 2014, 2015 and 2016, respectively, 28.8% were cesareans. Peripartum change in Hb was available for 17% of births. The incidence (95% CI) of PPH in 2014, 2015 and 2016 was, respectively: 1)6.0% (5.1, 7.0), 6.3% (5.4, 7.3) and 7.9% (6.9, 9.0) based on diagnostic codes; 2)7.9% (6.8, 9.0), 7.1% (6.2, 8.2) and 7.2% (6.3, 8.3) based on blood loss volumes; 3)2.4% (1.8, 3.1), 2.7% (2.1, 3.4) and 3.5% (2.9, 4.3) based on change in Hb; 4)11.3% (10.1, 12.6), 10.4% (9.3, 11.6) and 11.0% (9.9, 12.3) based on the combined definition. Differences in PPH incidence by year between definitions one and four, two and four and three and four were all statistically significant (McNemar p-values
Conclusion/ImplicationsIncidence varied widely according to definition and data availability, not to mention data quality. Our results highlight the need for a validated PPH indicator to enable monitoring. Future prospects include the validation of a diagnostic code based PPH indicator aided by text mining in electronic health records.

IntroductionFrailty is strongly associated with adverse health outcomes and health care costs in elders. However, we have almost no idea of the prevalence of frail older inpatients in Swiss hospitals. Hospital discharge data could contribute to predicting frailty in these patients, and eventually improving SwissDRGs system or casemix-adjustment.
Objectives and ApproachThe HFrailty project aimed to develop a predictive model of Fried's Frailty Phenotype (FFP) based on hospital discharge data. We linked Lausanne University Hospital (CHUV) discharge data to clinical data from the Lausanne cohort study (Lc65+) over the period 2004-2015. The Lc65+ is a longitudinal population-based cohort comprising three random samples of approximately 1500 Lausanne residents aged 65 to 70, born respectively before, during and after World War II. With stepwise and lasso penalized logistic regression, random forest and neural networks, we identified the best-performing model for predicting FFP using CHUV's data recorded within 12 months prior to frailty assessments.
ResultsAmong Lc65+ participants, 1649 were assessed for frailty and hospitalized at least once during the follow-up period, resulting in 3499 FFP assessments of which 544 were preceded by at least one hospitalization within 12 months. In total, 45.7% of the participants were men and 9.4% were frail (FFP score ≥ 3). As expected, prevalence of frailty increased with age from 4.1% in the 66-70 age group, to 5.3% and 10.5% in the 71-75 and 76-80 groups, respectively. Logistic regression with lasso penalty was finally the best model regarding both performance and complexity. It had an area under receiver operating curve of 0.67 to predict FFP based on detailed diagnosis and procedure codes.
Conclusion/ImplicationsHospital discharge data may be used to identify frail and non-frail individuals and estimate their prevalence in the Swiss non-institutionalized population. Our predictive model showed limited performance and could be improved. We are currently testing groups of diagnosis and procedure codes, as predictors, instead of detailed ones.

ABSTRACT
ObjectivesThe International Methodology Consortium for Coded Health Information (IMECCHI), an international collaboration of health services researchers, launched the IMECCHI-DATANETWORK initiative in October 2015. Its main objective is to enable replication of observational studies across countries through a distributed data infrastructure.
ApproachIn a distributed data infrastructure, individual raw data are not shared. Instead, data are converted locally using a common data model (CDM) and loaded into a common software for data processing and analysis. Whenever a study protocol is agreed upon and ethically approved, an ad hoc procedure is programmed - using that software - including the data processing steps needed to create the analytical dataset from the CDM: record linkage, case selection, sampling, matching, etc. The procedure is then shared and locally run by each partner to generate an analytical dataset of integrated data. Analytical datasets may then be shared and pooled for statistical analyses.
ResultsSix partners of the IMECCHI collaboration, located in countries across 4 continents (Canada, Denmark, Italy, New Zealand, South Korea, and Switzerland), currently participate in the initiative. They first conducted a survey to describe the origin, content, completeness and main attributes of each table in their original databases. Based on the results of the survey, a CDM was created, encompassing 4 tables of coded or structured data to be linked at the individual level using a common personal identifier: (1) characteristics of the subjects with dates of birth and death; (2) hospital discharge summaries with diagnosis and procedure codes, and admission, discharge and procedure dates; (3) drug dispensing information with date of dispensing, drug name, duration of the amount of active principle according to the Defined Daily Dose of the World Health Organization; (4) causes of death. In each table, additional attributes describe the coding systems in which the other attributes are coded. Using such specific attributes facilitates interoperability across multiple coding systems. The open source Java-based software, TheMatrix, which operates on flat csv files using a domain-specific programming language, was chosen to embed the ad hoc procedure.
ConclusionWithin the IMECCHI-DATANETWORK initiative, databases from various countries will be locally converted in a CDM which will facilitate study replication in a distributed fashion while granting interoperability across coding systems. Through such international data networks, data are empowered for creating results which are generalizable to multiple countries. Cross-border data sharing and international comparisons are also facilitated.

IntroductionThe International Methodology Consortium for Coded Health Information (IMeCCHI) is a collaboration of health services researchers who promote methodological advances in coded health information. The IMeCCHI-DATANETWORK initiative focuses on developing a multi-purpose distributed data infrastructure and common data model (CDM) to enable cross-border data sharing and international comparisons.
MethodsIMeCCHI consortium partners from six different countries – Canada, Denmark, Italy, New Zealand, South Korea, and Switzerland – used a questionnaire to describe their original databases which differ in size, structure, content and coding systems. To standardize these data, they agreed on a CDM and mapped their population-based databases to meet the CDM specifications. At the end of this process, local data had a more homogenous content and structure, which made them syntactically and semantically interoperable. Data transformation was performed using a common data management software called TheMatrix.
ResultsThe CDM encompasses four tables of structured data (person characteristics, hospitalizations, outpatient prescription medication and death), linked at the individual level through a person identifier. It can be used to answer research questions across countries using locally converted databases, which facilitates study replication in a distributed fashion. As a proof-of-concept study, an initial research question was addressed using an agreed protocol. Local data were transformed in csv files in the CDM structure and TheMatrix was tested to transform the standardized data from each partner into local analytical datasets. This allowed results to be shared between countries, whilst maintaining local control over each region's data.
ConclusionThe IMeCCHI-DATANETWORK, a model of a distributed data network, demonstrated that it is feasible to analyze international data using standardized analytical methods that enable independent analyses by regions, without relocating datasets thereby protecting local confidentiality obligations. The distributed data infrastructure can produce results that can be generalized to several countries, while facilitating cross-border data sharing and international comparisons.