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			<depositor_name>Clinical Intelligence Research Press</depositor_name>
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				<full_title>Journal of Artificial Intelligence for Healthcare Systems</full_title>
				<abbrev_title>J. Artif. Intell. Healthc. Syst.</abbrev_title>
				<issn>3149-8981</issn>
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					<year>2024</year>
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					<volume>3</volume>
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				<issue>2</issue>
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					<title>Causal Evidence for Telemedicine in Diabetes Control: Why Bayesian Structural Time Series on State-Level EHR Aggregates Should Guide Policy</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Ivan</given_name>
            <surname>Petrov</surname>
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            <given_name>Olga</given_name>
            <surname>Ivanova</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Dmitry</given_name>
            <surname>Smirnov</surname>
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								<publication_date>
					<year>2024</year>
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					  <unstructured_citation>Papadogeorgou G, Menchetti F, Choirat C, Wasfy JH, Zigler CM, Mealli F, et al. Evaluating federal policies using Bayesian time series models: estimating the causal impact of the hospital readmissions reduction program. Health Serv Outcomes Res Methodol. 2023;23(4):433-51.</unstructured_citation>
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					  <unstructured_citation>Thomas RL, Millett C, de Sousa Soares R, Hone T. More doctors, better health? A generalised synthetic control approach to estimating impacts of increasing doctors under Brazil&#039;s Mais Médicos programme. Soc Sci Med. 2024;358:117222.</unstructured_citation>
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					  <unstructured_citation>Wallach S, Saito S, Nuwagaba-Biribonwoha H, Dube L, Lamb MR. Synthetic controls for implementation science: opportunities for HIV program evaluation using routinely collected data. Curr HIV/AIDS Rep. 2024;21(3):140-51.</unstructured_citation>
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          					<citation key="rk-10.68159/n955173340-e1a363b2-ac65-4f73-bcf7-23c24933985d">
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					  <unstructured_citation>Olier I, Zhan Y, Liang X, Volovici V. Causal inference and observational data. BMC Med Res Methodol. 2023;23(1):227.</unstructured_citation>
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          					<citation key="rk-10.68159/n955173340-549b19dc-011d-4a5a-af05-a0acf411fe02">
					  <unstructured_citation>Barría-Sandoval C, Ferreira G, Lagos B, Bacigalupo CM. Assessing the effectiveness of quarantine measures during the COVID-19 pandemic in Chile using Bayesian structural time series models. Infect Dis Model. 2022;7(4):625-36.</unstructured_citation>
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          					<citation key="rk-10.68159/n955173340-b3d2f317-38bf-4644-b904-64d889a4cae3">
					  <unstructured_citation>Orozco-Beltran D, Sánchez-Molla M, Sanchez JJ, Mira JJ, ValCrònic Research Group. Telemedicine in primary care for patients with chronic conditions: the ValCrònic quasi-experimental study. J Med Internet Res. 2017;19(12):e400.</unstructured_citation>
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					  <unstructured_citation>Walker B, Stoecker C, Shao Y, Nauman E, Fort D, Shi L. Telehealth and Medicare type 2 diabetes care outcomes: evidence from Louisiana. Med Care. 2023;61(Suppl 4):S77-S82.</unstructured_citation>
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					  <unstructured_citation>Hong YR, Xie Z, Nguyen OT, Turner K, Walker AF. Telehealth service use and quality of care among US adults with diabetes: a cross-sectional study of the 2022 Health Information National Trends Survey. BMJ Open. 2024;14(10):e086418.</unstructured_citation>
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          					<citation key="rk-10.68159/n955173340-ee7022be-56a3-470d-a994-bdd056a98d25">
					  <unstructured_citation>Oviedo SA, McDonald B, Gander JC, Ali MK, Harding JL. Access to telehealth and changes in diabetes care patterns during the pandemic: evidence from a large integrated health system in the Southeast USA. BMJ Open Diabetes Res Care. 2024;12(1):e003866.</unstructured_citation>
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					  <unstructured_citation>Chiaranai C, Chularee S, Saokaew S, Bhatarasakoon P, Umnuaypornlert A, Chaomuang N, et al. Effectiveness of telehealth on glycemic control of patients with type 2 diabetes mellitus during COVID-19: systematic review and meta-analysis. Int J Nurs Stud Adv. 2024;6:100169.</unstructured_citation>
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					  <unstructured_citation>Alfarwan N, Hodkinson A, Panagioti M, Hassan L, Kontopantelis E. Clinical and cost effectiveness of telemedicine among patients with type 2 diabetes in primary care: systematic review and meta-analysis. Diabet Med. 2024;41(8):e15343.</unstructured_citation>
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					  <unstructured_citation>Eberle C, Stichling S. Clinical improvements by telemedicine interventions managing type 1 and type 2 diabetes: systematic meta-review. J Med Internet Res. 2021;23(2):e23244.</unstructured_citation>
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          					<citation key="rk-10.68159/n955173340-627b386d-6d48-4f45-8e80-dd38c37bc85a">
					  <unstructured_citation>Hangaard S, Laursen SH, Andersen JD, Kronborg T, Vestergaard P, Hejlesen O, et al. Effectiveness of telemedicine solutions for management of type 2 diabetes: systematic review, meta-analysis, and meta-regression. J Diabetes Sci Technol. 2023;17(3):794-825.</unstructured_citation>
						 <doi>10.1177/19322968221113456</doi> 					</citation>
          					<citation key="rk-10.68159/n955173340-e23597e8-c37e-4742-8e17-d8781cd08a45">
					  <unstructured_citation>Zhang A, Wang J, Wan X, Zhang Z, Zhao S, Guo Z, et al. Meta-analysis of effectiveness of telemedicine in glycemic management among patients with type 2 diabetes in primary care. Int J Environ Res Public Health. 2022;19(7):4173.</unstructured_citation>
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          					<citation key="rk-10.68159/n955173340-5fdaecd8-9647-4f84-9bb5-c587cc02a49b">
					  <unstructured_citation>Maaitah W, Abdelhay O, Tourkmani A, Azzeh M, Abu-Soud MS, Atiani S. Telemedicine interventions in type 2 diabetes management: protocol for systematic review and network meta-analysis. BMJ Open. 2024;14(2):e078100.</unstructured_citation>
						 <doi>10.1136/bmjopen-2024-078100</doi> 					</citation>
          					<citation key="rk-10.68159/n955173340-7677b829-187e-4671-9606-782e2481a49a">
					  <unstructured_citation>Silva-Almodóvar A, Nahata MC. Telehealth intervention to improve uptake of evidence-based medications among patients with type 2 diabetes and cardiovascular disease. Int J Environ Res Public Health. 2023;20(4):3613.</unstructured_citation>
						 <doi>10.3390/ijerph20043613</doi> 					</citation>
          					<citation key="rk-10.68159/n955173340-a2539322-d040-410f-abe9-c89e5bfdfa5d">
					  <unstructured_citation>Ko YA, Chen Z, Liu C, Hu Y, Quyyumi AA, Waller LA, et al. Developing a synthetic control group using electronic health records: application to a single-arm lifestyle intervention study. Prev Med Rep. 2021;24:101572.</unstructured_citation>
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          					<citation key="rk-10.68159/n955173340-7e428d6c-762d-4705-9e82-17a7fb05e435">
					  <unstructured_citation>Allen KS, Valvi N, Gibson PJ, McFarlane T, Dixon BE. Electronic health records for population health management: comparison of EHR-derived hypertension prevalence measures against survey data. Online J Public Health Inform. 2024;16(1):e48300.</unstructured_citation>
						 <doi>10.5210/ojphi.v16i1.48300</doi> 					</citation>
          					<citation key="rk-10.68159/n955173340-ae3d24b4-00ee-4bac-847e-e9a83c3a9d35">
					  <unstructured_citation>Hirsch AG, Conderino S, Crume TL, Liese AD, Bellatorre A, Bendik S, et al. Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: study protocol for the DiCAYA Network. BMJ Open. 2024;14(1):e073791.</unstructured_citation>
						 <doi>10.1136/bmjopen-2024-073791</doi> 					</citation>
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					  <unstructured_citation>Mardon R, Campione J, Nooney J, Merrill L, Johnson MJ Jr, Marker D, et al. State-level metabolic comorbidity prevalence and control among adults age 50-plus with diabetes: estimates from electronic health records and survey data in five states. Popul Health Metr. 2022;20(1):22.</unstructured_citation>
						 <doi>10.1186/s12963-022-00301-0</doi> 					</citation>
          					<citation key="rk-10.68159/n955173340-3633ae3f-8463-49c5-8cce-52b988b35949">
					  <unstructured_citation>Moreno-Iribas C, Sayon-Orea C, Delfrade J, Ardanaz E, Gorricho J, Burgui R, et al. Validity of type 2 diabetes diagnosis in a population-based electronic health record database. BMC Med Inform Decis Mak. 2017;17(1):34.</unstructured_citation>
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					  <unstructured_citation>He Q, Keith D, Eckhoff DO, Park C, Ng BP. Accessibility and utilization of telehealth services during the COVID-19 pandemic among Medicare beneficiaries by diabetes status. Res Gerontol Nurs. 2023;16(3):134-46.</unstructured_citation>
						 <doi>10.3928/19404921-20230215-01</doi> 					</citation>
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