Room
1er
étage - E130
Theme
Tools and methods for better health
Chair
Oliver Gruebner
Title
Tools
and methods for automatic detection of healthcare-related adverse events: a
systematic review
Name
Arthur Escher
Affiliation
Unisanté
Abstract
Background:Healthcare-related adverse events (AEs) are a prevalent and significant concern, occurring in approximately 10% of hospital stays and accounting for 15% of hospital expenses. Traditional screening methods rely on retrospective manual record reviews, demanding substantial human and temporal resources. The integration of artificial intelligence (AI) offers a promising avenue for more efficient detection of AEs.
Objective:
The objective is to systematically review both scientific and grey literature on automated tools for detecting healthcare-related adverse events.
Methods:
We conducted a comprehensive search using established Cochrane methodologies in Medline, Embase, CINAHL, Cochrane Library, Joanna Briggs Institute, Web of Science, and Google Scholar from 2009 to 2024. This search also included a review of references from selected articles.
Our inclusion criteria encompass all articles in French, English, and German related to healthcare-related adverse events and automated screening, without any exclusion based on study type or patient age.
Two independent reviewers will undertake study selection, data extraction, risk of bias assessment, and GRADE evaluation. Results will include: 1) Methodological characteristics such as the automated screening method, its metrics, comparison with non-automated methods, data sources, and patient selection criteria; 2) Characteristics of AEs like prevalence/incidence, types and classification, validation criteria, and severity; 3) Evaluation for potential inclusion in the Swiss quality framework, international comparisons, cost-benefit analysis, and examination of facilitators and barriers to implementation. Results will be categorized by healthcare setting (acute or long-term hospital stay, outpatient).
Results:
An initial search across various databases indicates a need to screen around 4,000 abstracts (prior to duplicate removal). Results will be presented at the conference.
Conclusions:
The review aims to identify or develop cost-effective and reliable automated methods for screening and monitoring healthcare adverse events across various settings. This will inform Swiss healthcare policy in Quality and Safety.
Title
Entwicklung
einer Smartphone Applikation zur benutzerfreundlichen Erfassung des
individuellen Gesundheitszustandes
Name
Claudia Huber
Affiliation
University
of Applied Sciences of Western Switzerland
Abstract
Hintergrund:Junge Erwachsene sind in ihrem Alltag hohen Anforderungen ausgesetzt, welche Auswirkungen auf ihre physische und psychische Gesundheit haben können. Im Rahmen eines Vorprojekts, welches von 06.2021 bis 01.2022 dauerte, wurde zusammen mit Studierenden der Fachhochschule Freiburg eine Smartphone Applikation «MonCoSaMo» (Monitoring de la Consultation Santé Mozaik) zur Erfassung des individuell erlebten Gesundheitszustandes entwickelt. Diese App umfasst auch zusätzliche Unterstützungsangebote. In einer erweiterten Anwendung und in einem Folgeprojekt wird diese App, die sich gleicherweise als Instrument zum Gesundheitsmonitoring von jungen Erwachsenen im Kanton Freiburg eignet, in einer Längsschnittstudie getestet werden. Hier wird die Entwicklung der App vorgestellt.
Methode:
Mit Hilfe von halbstrukturierten Fokusgruppeninterviews und einer
Fragebogenerhebung wurden bei einer gezielt ausgewählten Bevölkerungsgruppe
von jungen Erwachsenen der individuell erlebte Gesundheitszustand, die Bedürfnisse
an Unterstützungsangeboten und die Erwartungen an eine Smartphone Applikation
ermittelt.
Ergebnisse:
Insgesamt haben 37 von 71 angefragten Studierenden an der
Umfrage teilgenommen, was einer Rücklaufquote von 52% entspricht. Davon haben
15 Studierende zusätzlich an einem Fokusgruppeninterview mitgemacht. Dabei
wurden Fragen zu folgenden Themen erforscht: Stress, Ernährung, körperliche
Aktivität, Suchtmittelkonsum, Internet-Nutzung, Sexualität, Gewalt,
Allgemeine Gesundheit und Wohlbefinden. Die Datenanalyse der obigen Parameter
und die Rückmeldungen von Anwenderinnen und Anwendern zeigten, dass die
zweisprachige App (D/F) ein benutzerfreundliches Instrument zur Erfassung des
individuellen Gesundheitszustandes ist. Mit wenigen Anpassungen lässt es sich
auch bei einer grösseren, stärker diversifizierten Bevölkerungsgruppe
anwenden.
Schlussfolgerung:
Schliesslich ist die bestehende Smartphone Applikation
hilfreich für ein zweckmässiges Monitoring der Gesundheit von jungen Erwachsenen
im Kanton Freiburg. Daraus können zum einen gezielte und gesundheitsfördernde
und zum anderen präventive Massnahmen geplant und umgesetzt werden. Eine
regelmässige Datenerhebung ist notwendig, um Risiken und Ressourcen zu
erkennen und um gesundheitspolitisch über den Verlauf der Gesundheit junger
Erwachsener informieren zu können.
Title
De-Identification
of health data in compliance with Swiss legislation
Name
Julia Maurer
Affiliation
Swiss
Institute of Bioinformatics/SPHN
Abstract
For this reason, the Swiss Personalized Health Network (SPHN) launched the “Data de-identification Project” in 2021 and developed a first set of recommendations for the de-identification of health data. A revised version of the recommendations in line with the SPHN Interoperability Framework will be published soon, enabling data sharing in accordance with current Swiss legislation and data protection regulations.
The developed phased approach not only aims to cover the risk mitigation for re-identification, but also considers the management of a de-identification process that foresees the verification and the periodic review of the risk assessment performed. Hands-on support for defining project specific de-identification and re-identification risk is provided with a template to evaluate the use case and assess the risk according to the project specifications.
Most importantly, the recommendations demonstrate that the simple removal of the direct identifiers does not necessarily mean that the data can only be re-identified with disproportionate effort, as the risk from combination or other remaining risks are not taken into account. Therefore, any rule-based approach will have to be combined with a risk assessment, in order to satisfy Swiss law requirements.
First SPHN research projects recently successfully applied the de-identification approach and submitted it with the proposal to the ethics committees. The goal is to enhance the adoption of de-identification methods by providing training to the Swiss research community in collaboration with Swiss hospitals, thereby fostering its broad implementation.
Title
Détection
des hémorragies associées aux antithrombotiques chez les patients âgés
hospitalisés à partir du dossier médical informatisé : étude transversale
dans trois hôpitaux universitaires suisses.
Name
Marie-Annick Le Pogam
Affiliation
Centre de
Recherche et d’Innovation en Sciences Pharmaceutiques Cliniques, Hôpital
Universitaire de Lausanne, Lausanne, Suisse. Ecole des Sciences
Pharmaceutiques, Université de Genève, Genève, Suisse.
Abstract
Introduction:Les hémorragies liées aux antithrombotiques chez les patients âgés, souvent sous-déclarées, représentent des enjeux sanitaires et économiques majeurs. Le développement d'un système de détection automatisé via le dossier médical informatisé (DMI) peut renforcer la surveillance et la gestion des risques liés à ces médicaments fréquemment utilisés chez les séniors.
Ce projet, soutenu par le PNR74, visait à développer et valider un outil automatisé pour détecter les hémorragies sévères (HS) et cliniquement significatives (HCS) associées aux médicaments antithrombotiques chez les patients de 65 ans et plus, hospitalisés en soins aigus.
Méthode:
L'étude a inclus tous les séjours en soins aigus de patients de 65 ans et plus traités par antithrombotique en 2015/2016 dans trois hôpitaux universitaires suisses. Nous avons élaboré des algorithmes pour identifier les HS et HCS, basés sur les critères de l'International Society of Thrombosis and Haemostasis, en utilisant des données structurées (diagnostiques, procédures, résultats de laboratoire, médicaments) et non structurées (lettres de sortie) des DMI. Ces algorithmes combinaient des règles spécifiques et des techniques de traitement du langage naturel. Leur performance a été évaluée par rapport à une revue manuelle de 800 séjours aléatoires.
Résultats:
Parmi 36,039 séjours, les taux d'incidence des HS et HCS étaient de 8.3% et 15% respectivement, avec une mortalité intra-hospitalière de 1% pour les HS. Les contributions des diverses sources de données étaient : laboratoire (66.9% HS/62.9% HCS), codes CIM-10-GM (28.5% HS/35.4% HCS), transfusions sanguines (3.2% HS/1.7% HCS), et prescriptions antihémorragiques (1.4% HS). La validation des algorithmes se poursuit, avec des résultats attendus pour la conférence.
Conclusion:
L'approche automatisée combinant diverses sources de données du DMI représente une innovation majeure, promettant d'améliorer la sécurité des patients en facilitant la détection des effets indésirables des médicaments.
Title
Predicting
patients with high insurance expenditures for home care services using
machine learning
Name
Flurina Meier Schwarzer
Affiliation
Zurich
University of Applied Sciences, Swiss Tropical and Public Health Institute,
University of Basel
Abstract
Background:
The number of persons using home care services increased in Switzerland in
the past years and is expected to increase further. According to Swiss home
care service organisations especially the number of cases with high health
care use increased and they assert that the current Swiss tariff system
poorly reflects high users. Moreover, it remains poorly understood which
patient characteristics are linked to higher service use or high insurance
expenditures and home care service organisations have a poor basis to predict
which patient might be part of this group. The objective of this study was to
identify patient characteristics that predict insurance expenditures above
1000 CHF per month and to determine which patient characteristics are most
important to predict expenditures.
Methods:
This observational study was based on data from eight Swiss home
care service organisations. All patients undergoing routine assessments with
the interRAI Home-Care Switzerland (HCS) questionnaire during the inclusion
phase were included in the study. Besides data from the interRAI-HCS further
characteristics covering mostly the case environment were assessed.
Administrative data on service use in the three months following the
assessment was the basis to calculate insurance expenditures. 168 patient
features served as input variables and insurance expenditures above/below
1000 CHF per month as endpoint in our predictive classification models. The
aim was to find the best prediction model by evaluating six machine learning
methods (including random forests, neural networks and XGBoost).
Explainable-AI approach SHAP was used to explain the contribution of each
feature to the model prediction and to assess global feature importance.
Results:
1035 patients were included in our study (mean age: 80 years, SD:
11.6). Best predictions were found with the XGBoost model that resulted in a
ROC AUC value of 0.857 in the holdout set. The three patient characteristics
most influential for expenditure prediction were: strength of limitations for
dressing below the waist, use of meal services and needs for help with
medication management.
Discussion:
Machine learning is useful to predict insurance expenditures in
the home care services setting. However, the generalisability of our models
needs to be further evaluated in broader contexts. Our study can provide
guidance on which patient characteristics should be routinely collected by
organisations to predict insurance expenditures.