{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T22:06:11Z","timestamp":1792706771443,"version":"4.5.0"},"reference-count":33,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"10","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circ: Cardiovascular Quality and Outcomes"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Digital remote patient monitoring (RPM), such as home-based blood pressure, heart rate, or weight monitoring, enables longitudinal care outside traditional health care settings, especially in the vulnerable period after hospitalizations, with broad coverage of the service by payers. We sought to evaluate patterns of RPM service availability at US hospitals and the characteristics of hospitals and the counties they serve that are associated with the availability of these services.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>We used national data from the American Hospital Association Annual Survey from 2018 to 2022 to ascertain US hospitals offering RPM services for postdischarge or chronic care. We linked hospitals with their census-based county-level data to define the characteristics of the counties they serve, including sociodemographic features such as age distribution, racial\/ethnic composition, median household income, education level, and disability status. We used multivariable logistic regression to assess associations between hospital- and county-level characteristics and RPM availability, adjusting for hospital size, region, teaching status, and ownership.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>The study included 5644 hospitals. Over 5 years of study, there was a 40.3% increase in the number of hospitals offering RPM services, rising from 1364 (33.0%) hospitals in 2018 to 1797 (46.3%) in 2022. In 2022, hospitals with &gt;300 beds had 3.7-fold odds of offering RPM compared with those with &lt;100 beds (adjusted odds ratio, 3.71 [95% CI, 2.90\u20134.74]). Nonteaching hospitals had lower odds of RPM availability than teaching hospitals (adjusted odds ratio, 0.29 [95% CI, 0.19\u20130.44]), and rural hospitals had lower odds than urban hospitals (adjusted odds ratio, 0.49 [95% CI, 0.32\u20130.77]).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>In this national study of US hospitals, there has been a large increase in the availability of RPM services but with large variation among hospitals, with lower availability in hospitals serving low-income and rural counties.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/circoutcomes.125.012034","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T09:00:20Z","timestamp":1755594020000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":8,"title":["National Patterns of Remote Patient Monitoring Service Availability at US Hospitals"],"prefix":"10.1161","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1876-8304","authenticated-orcid":true,"given":"Aline F.","family":"Pedroso","sequence":"first","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine (A.F.P., Z.L., J.S.R., R.K.), Yale School of Medicine, New Haven, CT."},{"name":"Cardiovascular Data Science (CarDS) Lab (A.F.P., R.K.), Yale School of Medicine, New Haven, CT."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenqiu","family":"Lin","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine (A.F.P., Z.L., J.S.R., R.K.), Yale School of Medicine, New Haven, CT."},{"name":"Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT (Z.L., J.S.R., R.K.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9218-3320","authenticated-orcid":true,"given":"Joseph S.","family":"Ross","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine (A.F.P., Z.L., J.S.R., R.K.), Yale School of Medicine, New Haven, CT."},{"name":"Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT (Z.L., J.S.R., R.K.)."},{"name":"Department of Health Policy and Management (J.S.R.), Yale School of Public Health, New Haven, CT."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9467-6199","authenticated-orcid":true,"given":"Rohan","family":"Khera","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine (A.F.P., Z.L., J.S.R., R.K.), Yale School of Medicine, New Haven, CT."},{"name":"Cardiovascular Data Science (CarDS) Lab (A.F.P., R.K.), Yale School of Medicine, New Haven, CT."},{"name":"Section of Biomedical Informatics and Data Science (R.K.), Yale School of Medicine, New Haven, CT."},{"name":"Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT (Z.L., J.S.R., R.K.)."},{"name":"Section of Health Informatics, Department of Biostatistics (R.K.), Yale School of Public Health, New Haven, CT."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.2196\/34144"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1177\/20552076231185434"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cardfail.2024.04.018"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2020-040232"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002389"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2022.3043"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2022.1052408"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00490-9"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2021-051844"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1002\/ejhf.1709"},{"key":"e_1_3_3_12_2","unstructured":"2023 Focused Update of the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. 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