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Nevertheless, few studies have focused on predictors of postdischarge medication adherence.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>METHODS:\u003C\u002Fjats:title>\u003Cjats:p>The Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study used counseling and follow‐up to improve postdischarge medication safety. In this secondary data analysis, we analyzed predictors of self‐reported medication adherence after discharge. Based on an interview at 30‐days postdischarge, an adherence score was calculated as the mean adherence in the previous week of all regularly scheduled medications. Multivariable linear regression was used to determine the independent predictors of postdischarge adherence.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>RESULTS:\u003C\u002Fjats:title>\u003Cjats:p>The mean age of the 646 included patients was 61.2 years, and they were prescribed an average of 8 daily medications. The mean postdischarge adherence score was 95% (standard deviation [SD] = 10.2%). For every 10‐year increase in age, there was a 1% absolute increase in postdischarge adherence (95% confidence interval [CI] 0.4% to 2.0%). Compared to patients with private insurance, patients with Medicaid were 4.5% less adherent (95% CI −7.6% to −1.4%). For every 1‐point increase in baseline medication adherence score, as measured by the 4‐item Morisky score, there was a 1.6% absolute increase in postdischarge medication adherence (95% CI 0.8% to 2.4%). Surprisingly, health literacy was not an independent predictor of postdischarge adherence.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>CONCLUSIONS:\u003C\u002Fjats:title>\u003Cjats:p>In patients hospitalized for cardiovascular disease, predictors of lower medication adherence postdischarge included younger age, Medicaid insurance, and baseline nonadherence. 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2411",{},{"id":24,"text":763,"url":24,"identifiers":764},"10.1016\u002Fj.jpainsymman.2004.08.011",{"doi":763},{"id":24,"text":766,"url":24,"identifiers":767},"10.1007\u002Fs11606-012-2328-6",{"doi":766},{"id":24,"text":769,"url":24,"identifiers":770},"10.1001\u002Farchinternmed.2008.565",{"doi":769},{"id":24,"text":772,"url":24,"identifiers":773},"10.1007\u002FBF02597402",{"doi":772},{"id":24,"text":775,"url":24,"identifiers":776},"10.1111\u002Fj.1365-2923.2001.01018.x",{"doi":775},{"id":24,"text":778,"url":24,"identifiers":779},"10.1111\u002Fj.1365-2929.2005.02345.x",{"doi":778},{"id":24,"text":781,"url":24,"identifiers":782},"10.1016\u002Fj.acap.2009.12.006",{"doi":781},{"id":784,"createTime":785,"updateTime":785,"relativeEntities":786,"slug":787,"properties":788,"entityType":150,"verifyStatus":151,"verifyTime":785,"verifyNote":152,"languages":803,"translateLanguages":24,"viewCount":25,"primaryUrl":804,"fullTextUrl":24,"authors":805,"publicationType":270,"publisherRelationship":896,"citationCount":965,"citationInfo":966,"publishDate":969,"publishYear":967,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":970,"openAccess":24,"references":971,"isForceReanalyzing":474},"fe718b55-4ae0-4604-81c2-fd5d8b69ecfa","2024-11-26T11:58:19.842+00:00",[],"Hospital-Perceptions-of-Medicare-s-Sepsis-Quality-Reporting-Initiative",{"mag":789,"pmc":791,"openalex":793,"abstract":795,"title":797,"pm":799,"doi":801},{"VOID":790},"2773602544",{"VOID":792},"5830093",{"VOID":794},"W2773602544",{"EN":796},"\u003Cjats:sec>\u003Cjats:title>BACKGROUND\u003C\u002Fjats:title>\u003Cjats:p>In October 2015, the Centers for Medicare and Medicaid Services (CMS) implemented the Sepsis CMS Core Measure (SEP‐1) program, requiring hospitals to report data on the quality of care for their patients with sepsis.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>OBJECTIVE\u003C\u002Fjats:title>\u003Cjats:p>We sought to understand hospital perceptions of and responses to the SEP‐1 program.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>DESIGN\u003C\u002Fjats:title>\u003Cjats:p>A thematic content analysis of semistructured interviews with hospital quality officials.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>SETTING\u003C\u002Fjats:title>\u003Cjats:p>A stratified random sample of short‐stay, nonfederal, general acute care hospitals in the United States.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>SUBJECTS\u003C\u002Fjats:title>\u003Cjats:p>Hospital quality officers, including nurses and physicians.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>INTERVENTION\u003C\u002Fjats:title>\u003Cjats:p>None.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>MEASUREMENTS\u003C\u002Fjats:title>\u003Cjats:p>We completed 29 interviews before reaching content saturation.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>RESULTS\u003C\u002Fjats:title>\u003Cjats:p>Hospitals reported a variety of actions in response to SEP‐1, including new efforts to collect data, improve sepsis diagnosis and treatment, and manage clinicians' attitudes toward SEP‐1. These efforts frequently required dedicated resources to meet the program's requirements for treatment and documentation, which were thought to be complex and not consistently linked to patient‐centered outcomes. Most respondents felt that SEP‐1 was likely to improve sepsis outcomes. At the same time, they described specific changes that could improve its effectiveness, including allowing hospitals to focus on the treatment processes most directly associated with improved patient outcomes and better aligning the measure's sepsis definitions with current clinical definitions.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>CONCLUSIONS\u003C\u002Fjats:title>\u003Cjats:p>Hospitals are responding to the SEP‐1 program across a number of domains and in ways that consistently require dedicated resources. Hospitals are interested in further revisions to the program to alleviate the burden of the reporting requirements and help them optimize the effectiveness of their investments in quality‐improvement efforts.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>",{"EN":798},"Hospital Perceptions of Medicare's Sepsis Quality Reporting Initiative",{"VOID":800},"29236094",{"VOID":802},"10.12788\u002Fjhm.2929",[154],"https:\u002F\u002Fshmpublications.onlinelibrary.wiley.com\u002Fdoi\u002F10.12788\u002Fjhm.2929",[806,833,848,865],{"id":807,"sortIndex":25,"researcher":24,"roles":808,"affiliations":809,"properties":826,"displayName":830,"givenName":24,"familyName":24},"7e26874c-1b41-4ac4-bec2-091cb3d1b710",[],[810,818],{"id":811,"sortIndex":25,"affiliation":812,"properties":24},"e8940162-894f-4f36-ba23-8b2c4fd724bb",{"id":811,"createTime":24,"updateTime":24,"relativeEntities":813,"slug":24,"properties":814,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":817,"statistic":24},[],{"title":815},{"EN":816},"CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania",[],{"id":819,"sortIndex":176,"affiliation":820,"properties":24},"fe3d2567-02d7-42fa-adda-0a021759cf62",{"id":819,"createTime":24,"updateTime":24,"relativeEntities":821,"slug":24,"properties":822,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":825,"statistic":24},[],{"title":823},{"VI":824},"Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania",[],{"orcid":827,"title":829,"openalex":831},{"VOID":828},"https:\u002F\u002Forcid.org\u002F0000-0002-9840-0576",{"EN":830},"Ian J. Barbash",{"VOID":832},"A5015513026",{"id":834,"sortIndex":176,"researcher":24,"roles":835,"affiliations":836,"properties":843,"displayName":845,"givenName":24,"familyName":24},"1d46d9c0-0a9b-4cf5-a436-5eb2c4ec8044",[],[837],{"id":811,"sortIndex":25,"affiliation":838,"properties":24},{"id":811,"createTime":24,"updateTime":24,"relativeEntities":839,"slug":24,"properties":840,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":842,"statistic":24},[],{"title":841},{"EN":816},[],{"title":844,"openalex":846},{"EN":845},"Kimberly J. Rak",{"VOID":847},"A5108278964",{"id":849,"sortIndex":194,"researcher":24,"roles":850,"affiliations":851,"properties":858,"displayName":862,"givenName":24,"familyName":24},"18ae52e9-5970-4c5b-966c-598054877bff",[],[852],{"id":811,"sortIndex":25,"affiliation":853,"properties":24},{"id":811,"createTime":24,"updateTime":24,"relativeEntities":854,"slug":24,"properties":855,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":857,"statistic":24},[],{"title":856},{"EN":816},[],{"orcid":859,"title":861,"openalex":863},{"VOID":860},"https:\u002F\u002Forcid.org\u002F0000-0001-9137-0928",{"EN":862},"Courtney C. 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Accessed on January 30 2017.",{},{"id":24,"text":1003,"url":24,"identifiers":1004},"10.2307\u002F798843",{"doi":1003},{"id":24,"text":1006,"url":24,"identifiers":1007},"10.1177\u002F1049732315576699",{"doi":1006},{"id":24,"text":1009,"url":24,"identifiers":1010},"10.1177\u002F1049732316665344",{"doi":1009},{"id":24,"text":1012,"url":24,"identifiers":1013},"10.1513\u002FAnnalsATS.201509‐625ED",{"doi":1012},{"id":24,"text":1015,"url":24,"identifiers":1016},"Guest G, 2008, Handbook for Team‐Based Qualitative Research",{},{"id":24,"text":1018,"url":24,"identifiers":1019},"10.1377\u002Fhlthaff.2010.1111",{"doi":1018},{"id":24,"text":1021,"url":24,"identifiers":1022},"10.1056\u002FNEJMsb1205420",{"doi":1021},{"id":24,"text":1024,"url":24,"identifiers":1025},"10.1016\u002Fj.ajem.2016.06.022",{"doi":1024},{"id":24,"text":1027,"url":24,"identifiers":1028},"10.1161\u002FCIRCOUTCOMES.112.965210",{"doi":1027},{"id":24,"text":1030,"url":24,"identifiers":1031},"10.1136\u002Fbmjqs‐2012‐001081",{"doi":1030},{"id":24,"text":1033,"url":24,"identifiers":1034},"10.1056\u002FNEJMp1408345",{"doi":1033},{"id":24,"text":1036,"url":24,"identifiers":1037},"10.1016\u002Fj.chest.2016.07.004",{"doi":1036},{"id":24,"text":1039,"url":24,"identifiers":1040},"10.1164\u002Frccm.201407‐1345LE",{"doi":1039},{"id":24,"text":1042,"url":24,"identifiers":1043},"10.1001\u002Fjama.2012.94856",{"doi":1042},{"id":24,"text":1045,"url":24,"identifiers":1046},"10.1001\u002Fjama.295.10.1168",{"doi":1045},{"id":24,"text":1048,"url":24,"identifiers":1049},"10.1097\u002FMLR.0b013e31829fa92a",{"doi":1048},{"id":24,"text":1051,"url":24,"identifiers":1052},"10.1097\u002FCCM.0000000000002255",{"doi":1051},{"id":24,"text":1054,"url":24,"identifiers":1055},"10.1007\u002Fs00134‐003‐1662‐x",{"doi":1054},{"id":24,"text":1057,"url":24,"identifiers":1058},"10.1001\u002Fjama.2016.6374",{"doi":1057},{"id":24,"text":1060,"url":24,"identifiers":1061},"10.1001\u002Fjamainternmed.2014.5161",{"doi":1060},{"id":1063,"createTime":1064,"updateTime":1064,"relativeEntities":1065,"slug":1066,"properties":1067,"entityType":150,"verifyStatus":151,"verifyTime":1082,"verifyNote":152,"languages":1083,"translateLanguages":24,"viewCount":25,"primaryUrl":1084,"fullTextUrl":24,"authors":1085,"publicationType":270,"publisherRelationship":1201,"citationCount":1270,"citationInfo":1271,"publishDate":1274,"publishYear":1272,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":1275,"openAccess":24,"references":1276,"isForceReanalyzing":474},"802e5ba5-d769-4089-b273-71cdad165b1b","2024-10-17T03:08:08.615+00:00",[],"Comparison-of-mental-status-scales-for-predicting-mortality-on-the-general-wards",{"mag":1068,"pmc":1070,"openalex":1072,"abstract":1074,"title":1076,"pm":1078,"doi":1080},{"VOID":1069},"1949483121",{"VOID":1071},"4751584",{"VOID":1073},"W1949483121",{"EN":1075},"\u003Cjats:sec>\u003Cjats:title>BACKGROUND\u003C\u002Fjats:title>\u003Cjats:p>Altered mental status is a significant predictor of mortality in inpatients. Several scales exist to characterize mental status, including the AVPU (Alert, responds to Voice, responds to Pain, Unresponsive) scale, which is used in many early‐warning scores in the general‐ward setting. The use of the Glasgow Coma Scale (GCS) and Richmond Agitation Sedation Scale (RASS) is not well established in this population.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>OBJECTIVE\u003C\u002Fjats:title>\u003Cjats:p>To compare the accuracies of AVPU, GCS, and RASS for predicting inpatient mortality.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>DESIGN\u003C\u002Fjats:title>\u003Cjats:p>Retrospective cohort study.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>SETTING\u003C\u002Fjats:title>\u003Cjats:p>Single, urban, academic medical center.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>PARTICIPANTS\u003C\u002Fjats:title>\u003Cjats:p>Adult inpatients on the general wards.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>MEASUREMENTS\u003C\u002Fjats:title>\u003Cjats:p>Nurses recorded GCS and RASS on consecutive adult hospitalizations. AVPU was extracted from the eye subscale of the GCS. We compared the accuracies of each scale for predicting in‐hospital mortality within 24 hours of a mental‐status observation using area under the receiver operating characteristic curves (AUC).\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>RESULTS\u003C\u002Fjats:title>\u003Cjats:p>There were 295,974 paired observations of GCS and RASS obtained from 26,873 admissions; 417 (1.6%) resulted in in‐hospital death. GCS and RASS more accurately predicted mortality than AVPU (AUC 0.80 and 0.82, respectively, vs 0.73; \u003Cjats:italic>P\u003C\u002Fjats:italic> < 0.001 for both comparisons). Simultaneous use of GCS and RASS produced an AUC of 0.85 (95% confidence interval: 0.82‐0.87, \u003Cjats:italic>P\u003C\u002Fjats:italic> < 0.001 when compared to all 3 scales).\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>CONCLUSIONS\u003C\u002Fjats:title>\u003Cjats:p>In ward patients, both GCS and RASS were significantly more accurate predictors of mortality than AVPU. In addition, combining GCS and RASS was more accurate than any scale alone. Routine tracking of GCS and\u002For RASS on general wards may improve the accuracy of detecting clinical deterioration. \u003Cjats:italic>Journal of Hospital Medicine\u003C\u002Fjats:italic> 2015;10:658–663. © 2015 Society of Hospital Medicine\u003C\u002Fjats:p>\u003C\u002Fjats:sec>",{"EN":1077},"Comparison of mental‐status scales for predicting mortality on the general wards",{"VOID":1079},"26374471",{"VOID":1081},"10.1002\u002Fjhm.2415","2024-10-17T03:08:08.614+00:00",[154],"https:\u002F\u002Fshmpublications.onlinelibrary.wiley.com\u002Fdoi\u002F10.1002\u002Fjhm.2415",[1086,1103,1120,1137,1152,1167,1186],{"id":1087,"sortIndex":25,"researcher":24,"roles":1088,"affiliations":1089,"properties":1098,"displayName":1100,"givenName":24,"familyName":24},"e8b69849-4dbd-49ac-a7a0-64781134d54d",[],[1090],{"id":1091,"sortIndex":25,"affiliation":1092,"properties":24},"c3e34916-3200-42a4-a05d-6fc06221a374",{"id":1091,"createTime":24,"updateTime":24,"relativeEntities":1093,"slug":24,"properties":1094,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1097,"statistic":24},[],{"title":1095},{"EN":1096},"Section of Hospital Medicine University of Chicago Chicago Illinois",[],{"title":1099,"openalex":1101},{"EN":1100},"Frank J. 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Clinical characteristics, health outcomes, and overall health burden were compared across definitions and stratified by age.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>RESULTS\u003C\u002Fjats:title>\u003Cjats:p>Of that population, 10.3% of individuals were common to all definitions. High users based on number of inpatient episodes were more likely to be admitted for acute conditions, with most high users based on length of stay admitted for mental health‐related conditions, while those based on costs were more likely to have hospitalizations resulting in death (9.3%). High‐episode individuals accounted for 16.6% of all inpatient episodes, high‐length of stay individuals for 46.4% of all hospital days, and high‐cost individuals for 38.9% of total cost.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>CONCLUSIONS\u003C\u002Fjats:title>\u003Cjats:p>Three definitions of high users of inpatient services captured significantly different groups of patients. This has implications for targeting subgroups for intervention and highlights important considerations for selecting the most suitable definition for a given objective.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>",{"EN":1389},"Comparison of Methods to Define High Use of Inpatient Services Using Population‐Based Data",{"VOID":1391},"28786424",{"VOID":1393},"10.12788\u002Fjhm.2778","2024-10-13T17:35:53.703+00:00",[154],"https:\u002F\u002Fshmpublications.onlinelibrary.wiley.com\u002Fdoi\u002F10.12788\u002Fjhm.2778",[1398,1417,1442,1465,1482,1499,1524],{"id":1399,"sortIndex":25,"researcher":24,"roles":1400,"affiliations":1401,"properties":1410,"displayName":1414,"givenName":24,"familyName":24},"9038b84b-b879-4b34-91f8-fa12dd637965",[],[1402],{"id":1403,"sortIndex":25,"affiliation":1404,"properties":24},"a7550804-a6f5-485a-ad39-fd51b64d3a96",{"id":1403,"createTime":24,"updateTime":24,"relativeEntities":1405,"slug":24,"properties":1406,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1409,"statistic":24},[],{"title":1407},{"VI":1408},"Department of Medicine, Cumming School of Medicine, University of Calgary, Alberta, Canada",[],{"orcid":1411,"title":1413,"openalex":1415},{"VOID":1412},"https:\u002F\u002Forcid.org\u002F0000-0002-1210-5294",{"EN":1414},"James Wick",{"VOID":1416},"A5013668178",{"id":1418,"sortIndex":176,"researcher":24,"roles":1419,"affiliations":1420,"properties":1435,"displayName":1439,"givenName":24,"familyName":24},"7be78d23-d05f-466b-8b30-780e1e5ec1ab",[],[1421,1429],{"id":1422,"sortIndex":25,"affiliation":1423,"properties":24},"9a4613d9-df74-414a-9f13-e6797180abf9",{"id":1422,"createTime":24,"updateTime":24,"relativeEntities":1424,"slug":24,"properties":1425,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1428,"statistic":24},[],{"title":1426},{"VI":1427},"Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Alberta, Canada",[],{"id":1403,"sortIndex":176,"affiliation":1430,"properties":24},{"id":1403,"createTime":24,"updateTime":24,"relativeEntities":1431,"slug":24,"properties":1432,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1434,"statistic":24},[],{"title":1433},{"VI":1408},[],{"orcid":1436,"title":1438,"openalex":1440},{"VOID":1437},"https:\u002F\u002Forcid.org\u002F0000-0001-6818-4385",{"EN":1439},"Brenda R. 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Across 13 studies evaluating 1727 patients, no significant difference in clinical efficacy was observed (d = 1.6% [95% confidence interval (CI), ‐1.0%‐4.2%]). No significant difference was detected in microbiologic cure (8 studies, d = 1.2% [95% CI, ‐4.1%‐6.4%]), short‐term mortality (8 studies, d = 0.3% [95% CI, ‐1.2%‐1.8%]), longer‐term mortality (3 studies, d = ‐0.4% [95% CI, ‐6.3%‐5.5%]), or recurrence (10 studies, d = 2.1% [95% CI, ‐1.2%‐5.3%]). Heterogeneity across studies was not significant for any of the primary outcomes.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>CONCLUSIONS\u003C\u002Fjats:title>\u003Cjats:p>Based on the available literature, shorter courses of antibiotics can be safely utilized in hospitalized patients with common infections, including pneumonia, urinary tract infection, and intra‐abdominal infection, to achieve clinical and microbiologic resolution without adverse effects on mortality or recurrence.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>",{"EN":1711},"Shorter Versus Longer Courses of Antibiotics for Infection in Hospitalized Patients: A Systematic Review and Meta‐Analysis",{"VOID":1713},"29370318",{"VOID":1715},"10.12788\u002Fjhm.2905",[154],"https:\u002F\u002Fshmpublications.onlinelibrary.wiley.com\u002Fdoi\u002F10.12788\u002Fjhm.2905",[1719,1752,1769,1786],{"id":1720,"sortIndex":25,"researcher":24,"roles":1721,"affiliations":1722,"properties":1747,"displayName":1749,"givenName":24,"familyName":24},"7041c282-59b4-4fbb-bbfc-4b829872fcb8",[],[1723,1731,1739],{"id":1724,"sortIndex":25,"affiliation":1725,"properties":24},"b3133974-d330-4bf3-929c-2e056b16d9df",{"id":1724,"createTime":24,"updateTime":24,"relativeEntities":1726,"slug":24,"properties":1727,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1730,"statistic":24},[],{"title":1728},{"VI":1729},"Department of Internal Medicine, University of Cincinnati, Cincinnati, Ohio.",[],{"id":1732,"sortIndex":176,"affiliation":1733,"properties":24},"c4bf4c5e-5b33-4a15-838d-7cfea3eb1dd1",{"id":1732,"createTime":24,"updateTime":24,"relativeEntities":1734,"slug":24,"properties":1735,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1738,"statistic":24},[],{"title":1736},{"VI":1737},"Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan",[],{"id":1740,"sortIndex":194,"affiliation":1741,"properties":24},"956793ca-4680-48c0-af0a-78fed19f1f93",{"id":1740,"createTime":24,"updateTime":24,"relativeEntities":1742,"slug":24,"properties":1743,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1746,"statistic":24},[],{"title":1744},{"VI":1745},"Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio",[],{"title":1748,"openalex":1750},{"EN":1749},"Stephanie Royer",{"VOID":1751},"A5018198380",{"id":1753,"sortIndex":176,"researcher":24,"roles":1754,"affiliations":1755,"properties":1762,"displayName":1766,"givenName":24,"familyName":24},"1ad02cde-6df4-46cf-ae8f-35141724cae6",[],[1756],{"id":1732,"sortIndex":25,"affiliation":1757,"properties":24},{"id":1732,"createTime":24,"updateTime":24,"relativeEntities":1758,"slug":24,"properties":1759,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1761,"statistic":24},[],{"title":1760},{"VI":1737},[],{"orcid":1763,"title":1765,"openalex":1767},{"VOID":1764},"https:\u002F\u002Forcid.org\u002F0000-0003-1541-8840",{"EN":1766},"Kimberley M. 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of Economic 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fatigue from frequent nonactionable physiologic monitor alarms is frequently named as a threat to patient safety.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>PURPOSE\u003C\u002Fjats:title>\u003Cjats:p>To critically examine the available literature relevant to alarm fatigue.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>DATA SOURCES\u003C\u002Fjats:title>\u003Cjats:p>Articles published in English, Spanish, or French between January 1980 and April 2015 indexed in PubMed, Cumulative Index to Nursing and Allied Health Literature, Scopus, Cochrane Library, Google Scholar, and \u003Cjats:ext-link xmlns:xlink=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxlink\" xlink:href=\"http:\u002F\u002FClinicalTrials.gov\">ClinicalTrials.gov\u003C\u002Fjats:ext-link>.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>STUDY SELECTION\u003C\u002Fjats:title>\u003Cjats:p>Articles focused on hospital physiologic monitor alarms addressing any of the following: (1) the proportion of alarms that are actionable, (2) the relationship between alarm exposure and nurse response time, and (3) the effectiveness of interventions in reducing alarm frequency.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>DATA EXTRACTION\u003C\u002Fjats:title>\u003Cjats:p>We extracted data on setting, collection methods, proportion of alarms determined to be actionable, nurse response time, and associations between interventions and alarm rates.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>DATA SYNTHESIS\u003C\u002Fjats:title>\u003Cjats:p>Our search produced 24 observational studies focused on alarm characteristics and response time and 8 studies evaluating interventions. Actionable alarm proportion ranged from <1% to 36% across a range of hospital settings. Two studies showed relationships between high alarm exposure and longer nurse response time. Most intervention studies included multiple components implemented simultaneously. Although studies varied widely, and many had high risk of bias, promising but still unproven interventions include widening alarm parameters, instituting alarm delays, and using disposable electrocardiographic wires or frequently changed electrocardiographic electrodes.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>CONCLUSIONS\u003C\u002Fjats:title>\u003Cjats:p>Physiologic monitor alarms are commonly nonactionable, and evidence supporting the concept of alarm fatigue is emerging. Several interventions have the potential to reduce alarms safely, but more rigorously designed studies with attention to possible unintended consequences are needed. \u003Cjats:italic>Journal of Hospital Medicine\u003C\u002Fjats:italic> 2016;11:136–144. © 2015 Society of Hospital Medicine\u003C\u002Fjats:p>\u003C\u002Fjats:sec>",{"EN":2057},"Systematic Review of Physiologic Monitor Alarm Characteristics and Pragmatic Interventions to Reduce Alarm 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Sixty‐seven ischemic strokes were identified. The rate of stroke at 1 year after hip operation was 3.9%. In univariate analysis, history of atrial fibrillation (hazard ratio [HR], 2.16; \u003Cjats:italic>P\u003C\u002Fjats:italic> = 0.005), hip fracture repair vs. total hip arthroplasty (HR, 3.80; \u003Cjats:italic>P\u003C\u002Fjats:italic> < 0.001), age 75 years or older (HR, 2.20; \u003Cjats:italic>P\u003C\u002Fjats:italic> = 0.02), aspirin use (HR, 1.8; \u003Cjats:italic>P\u003C\u002Fjats:italic> = 0.01), and history of previous stroke (HR, 4.18; \u003Cjats:italic>P\u003C\u002Fjats:italic> < 0.001) were significantly associated with increased risk of stroke. In multivariable analysis, history of stroke (HR, 3.27; \u003Cjats:italic>P\u003C\u002Fjats:italic> < 0.001) and hip fracture repair (HR, 2.74; \u003Cjats:italic>P\u003C\u002Fjats:italic> = 0.004) were strong predictors of postoperative stroke.\u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>CONCLUSIONS:\u003C\u002Fjats:title>\u003Cjats:p>This population‐based historical cohort of patients with hip operation had a 3.9% cumulative probability of ischemic stroke over the first postoperative year. Hip fracture repair and history of stroke were the strongest predictors of this complication. 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