{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T06:32:28Z","timestamp":1777876348452,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2013,9,1]],"date-time":"2013-09-01T00:00:00Z","timestamp":1377993600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Telemedicine and e-Health"],"published-print":{"date-parts":[[2013,9]]},"abstract":"\n \n Objective:<\/jats:italic>\n <\/jats:bold>\n To explore association of patient characteristics and telehealth alert data with all-cause key medical events (KMEs) of emergency department (ED) visits and hospitalizations as well as cardiac-related KMEs of ED visits, hospitalizations, and medication changes.<\/jats:italic>\n \n Materials and Methods:<\/jats:italic>\n <\/jats:bold>\n A 6-month retrospective study was conducted of electronic patient records of heart failure (HF) patients using telehealth services at a Massachusetts home health agency. Data collected included patient demographic, psychosocial, disease severity factors and telehealth vital signs alerts. Association between patient characteristics and KMEs was analyzed by Generalized Estimating Equations.<\/jats:italic>\n \n Results:<\/jats:italic>\n <\/jats:bold>\n The sample comprised 168 patients with a mean age of 83 years, 56% females, and 96% white. Ninety-nine cardiac-related KMEs and 87 all-cause KMEs were recorded for the subjects. Odds of a cardiac-related KME increased by 161% with the presence of valvular co-morbidity (<\/jats:italic>\n p=\n 0.001) and 106% with increased number of telehealth alerts (adjusted<\/jats:italic>\n p<\n 0.0001). Odds of an all-cause KME increased by 124% (<\/jats:italic>\n p=\n 0.02), 127% (<\/jats:italic>\n p=\n 0.01), and 70% (adjusted<\/jats:italic>\n p<\n 0.0001) with the presence of cancer co-morbidity, anxiety, and increased number of telehealth alerts, respectively. Overall, only 3% of all telehealth alerts were associated with KMEs.<\/jats:italic>\n \n Conclusions:<\/jats:italic>\n <\/jats:bold>\n The very low proportion of telehealth vital sign alerts associated with KMEs indicates that telehealth alerts alone cannot inform the need for intervention within the larger context of HF care delivery in the homecare setting. Patient-relevant data such as psychosocial and symptom status, involvement with HF self-management, and presence of co-morbidities could further inform the need for interventions for HF patients in the homecare setting.<\/jats:italic>\n <\/jats:p>","DOI":"10.1089\/tmj.2012.0307","type":"journal-article","created":{"date-parts":[[2013,6,28]],"date-time":"2013-06-28T12:00:27Z","timestamp":1372420827000},"page":"664-670","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":15,"title":["A Retrospective Study on Patient Characteristics and Telehealth Alerts Indicative of Key Medical Events for Heart Failure Patients at a Home Health Agency"],"prefix":"10.1177","volume":"19","author":[{"given":"Kavita","family":"Radhakrishnan","sequence":"first","affiliation":[{"name":"University of Texas at Austin"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kathryn","family":"Bowles","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandra","family":"Hanlon","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maxim","family":"Topaz","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesse","family":"Chittams","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2013,9,1]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"e2","article-title":"American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics\u20142012 update: A report from the American Heart Association","volume":"125","author":"Roger VL","year":"2012","unstructured":"Roger VL, , Go AS, , Lloyd-Jones DM, , Benjamin EJ, , Berry JD, , Borden WB, , Bravata DM, , Dai S, , Ford ES, , Fox CS, , Fullerton HJ, , Gillespie C, , Hailpern SM, , Heit JA, , Howard VJ, , Kissela BM, , Kittner SJ, , Lackland DT, , Lichtman JH, , Lisabeth LD, , Makuc DM, , Marcus GM, , Marelli A, , Matchar DB, , Moy CS, , Mozaffarian D, , Mussolino ME, , Nichol G, , Paynter NP, , Soliman EZ, , Sorlie PD, , Sotoodehnia N, , Turan TN, , Virani SS, , Wong ND, , Woo D, , Turner MB. American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics\u20142012 update: A report from the American Heart Association. Circulation, 2012; 125:e2\u2013e220. 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