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dc.contributor.authorPerla, Rocco J.
dc.contributor.authorHohmann, Samuel F.
dc.contributor.authorAnnis, Karen
dc.date2022-08-11T08:08:31.000
dc.date.accessioned2022-08-23T15:57:58Z
dc.date.available2022-08-23T15:57:58Z
dc.date.issued2013-09-01
dc.date.submitted2015-03-24
dc.identifier.citationJ Healthc Qual. 2013 Sep-Oct;35(5):20-31. doi: 10.1111/jhq.12027. <a href="http://dx.doi.org/10.1111/jhq.12027">Link to article on publisher's site</a>
dc.identifier.issn1062-2551 (Linking)
dc.identifier.doi10.1111/jhq.12027
dc.identifier.pmid24004036
dc.identifier.urihttp://hdl.handle.net/20.500.14038/30326
dc.description.abstractHospitals often have limited ability to obtain primary clinical data from electronic health records to use in assessing quality and safety. We outline a new model that uses administrative data to gauge the safety of care at the hospital level. The model is based on a set of highly undesirable events (HUEs) defined using administrative data and can be customized to address the priorities and needs of different users. Patients with HUEs were identified using discharge abstracts from July 1, 2008 through June 30, 2010. Diagnoses were classified as HUEs based on the associated present-on-admission status. The 2-year study population comprised more than 6.5 million discharges from 161 hospitals. The proportion of hospitalizations including at least one HUE during the 24-month study period varied greatly among hospitals, with a mean of 7.74% (SD 2.3%) and a range of 13.32% (max, 15.31%; min, 1.99%). The whole-patient measure of safety provides a global measure to use in assessing hospitals with the patient's entire care experience in mind. As administrative and clinical datasets become more consistent, it becomes possible to use administrative data to compare the rates of HUEs across organizations and to identify opportunities for improvement.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=24004036&dopt=Abstract">Link to Article in PubMed</a>
dc.relation.urlhttp://dx.doi.org/10.1111/jhq.12027
dc.subjectClinical Coding
dc.subject*Hospitalization
dc.subjectHumans
dc.subjectMedical Errors
dc.subject*Patient Safety
dc.subjectProbability
dc.subjectSafety Management
dc.subjectUnited States
dc.subjectHealth and Medical Administration
dc.subjectHealth Services Administration
dc.titleWhole-patient measure of safety: using administrative data to assess the probability of highly undesirable events during hospitalization
dc.typeJournal Article
dc.source.journaltitleJournal for healthcare quality : official publication of the National Association for Healthcare Quality
dc.source.volume35
dc.source.issue5
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/596
dc.identifier.contextkey6889262
html.description.abstract<p>Hospitals often have limited ability to obtain primary clinical data from electronic health records to use in assessing quality and safety. We outline a new model that uses administrative data to gauge the safety of care at the hospital level. The model is based on a set of highly undesirable events (HUEs) defined using administrative data and can be customized to address the priorities and needs of different users. Patients with HUEs were identified using discharge abstracts from July 1, 2008 through June 30, 2010. Diagnoses were classified as HUEs based on the associated present-on-admission status. The 2-year study population comprised more than 6.5 million discharges from 161 hospitals. The proportion of hospitalizations including at least one HUE during the 24-month study period varied greatly among hospitals, with a mean of 7.74% (SD 2.3%) and a range of 13.32% (max, 15.31%; min, 1.99%). The whole-patient measure of safety provides a global measure to use in assessing hospitals with the patient's entire care experience in mind. As administrative and clinical datasets become more consistent, it becomes possible to use administrative data to compare the rates of HUEs across organizations and to identify opportunities for improvement.</p>
dc.identifier.submissionpathfaculty_pubs/596
dc.contributor.departmentDepartment of Quantitative Health Sciences
dc.source.pages20-31


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