{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:43:12Z","timestamp":1760143392986,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T00:00:00Z","timestamp":1707264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Molecular genetic techniques allow for the diagnosing of hereditary diseases and congenital abnormalities prenatally. A high variability of treatments exists, engendering an inappropriate clinical response, an inefficient use of resources, and the violation of the principle of the equality of treatment for equal needs. The proposed framework is based on modeling clinical pathways that contribute to identifying major causes of variability in treatments justified by the clinical needs\u2019 variability as well as depending on individual characteristics. An electronic data collection method for high-risk pregnant women addressing genetic facilities and laboratories was implemented. The collected data were analyzed retrospectively with two aims. The first is to identify how the whole activity of genetic services can be broken down into different clinical pathways. This was performed by building a flow chart with the help of doctors. The second aim consists of measuring the variability, within and among, the different paths due to individual characteristics. A set of statistical models was developed to determine the impact of the patient characteristics on the clinical pathway and its length. The results show the importance of considering these characteristics together with the clinical information to define the care pathway and the use of resources.<\/jats:p>","DOI":"10.3390\/a17020075","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T06:12:58Z","timestamp":1707286378000},"page":"75","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the Impact of Patient Characteristics on Genetic Clinical Pathways: A Regression Approach"],"prefix":"10.3390","volume":"17","author":[{"given":"Stefano","family":"Alderighi","sequence":"first","affiliation":[{"name":"UK Endorsement Board, London E14 4PU, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6532-6747","authenticated-orcid":false,"given":"Paolo","family":"Landa","sequence":"additional","affiliation":[{"name":"D\u00e9partement des Operations et Syst\u00e8mes des D\u00e9cision, Universit\u00e9 Laval, Quebec City, QC G1V 0A6, Canada"},{"name":"Centre de Recherche du Centre Hospitali\u00e8re Universitaire de Qu\u00e9bec, Axe Sant\u00e9 des Populations et Pratiques Optimales en Sant\u00e9, Universit\u00e9 Laval, Quebec City, QC G1V 0A6, Canada"},{"name":"Groupe de Recherche en \u00c9cologie Buccale (GREB), Universit\u00e9 Laval, Quebec City, QC G1V 0A6, Canada"},{"name":"Centre Interuniversitaire de Recherche sur les R\u00e9seaux D\u2019entreprise, La Logistique et le Transport (CIRRELT), Universit\u00e9 Laval, Qu\u00e9bec City, QC G1V 0A6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4261-4495","authenticated-orcid":false,"given":"Elena","family":"T\u00e0nfani","sequence":"additional","affiliation":[{"name":"Department of Economics (DIEC), University of Genoa, 16126 Genoa, Italy"}]},{"given":"Angela","family":"Testi","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine and Medical Specialties (DIMI), University of Genoa, 16132 Genoa, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gartner, J.B., Abasse, K.S., Bergeron, F., Landa, P., Lemaire, C., and C\u00f4t\u00e9, A. 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