{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"Essays on the Impact of Health Information Technology on Patient Outcomes","metadata":[{"label":"dc.description.sponsorship","value":"This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree."},{"label":"dc.format","value":"Monograph"},{"label":"dc.format.medium","value":"Electronic Resource"},{"label":"dc.identifier.uri","value":"http://hdl.handle.net/11401/77407"},{"label":"dc.language.iso","value":"en_US"},{"label":"dc.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.abstract","value":"This dissertation consists of three chapters surrounding the impacts of health information technology systems (HIT) on hospital inpatient outcomes.  In an effort to eliminate inefficiencies in the US health care sector, policymakers have made a concerted effort to encourage hospitals and physicians to adopt health information technology systems. In Chapter 2, I construct a unique dataset on health information technology adoption and health outcomes in New York State to conduct a hospital level analysis identifying the impact of adopting HIT on inpatient outcomes (rates of adverse drug events and severity-adjusted mortality). Unlike previous studies, the patient population is not restricted to Medicare patients, but covers all ages and insurance types. After controlling for unobserved hospital quality and endogenous HIT adoption, my results suggest that a hospital's severity-adjusted mortality decreases by 0.3 percentage points. When restricted to the Medicare patients, I find HIT adoption lowers a hospital's severity-adjusted mortality rate by 0.5 percentage points. I find HIT to have no significant effect on the rate of ADEs. In Chapter 3, I extend the analysis of Chapter 2 to conduct a patient level analysis identifying the impact of adopting HIT on inpatient mortality for pneumonia, COPD, and CHF inpatients. The econometric analysis requires the use of a binary outcome and binary endogenous variable, and presents challenges in estimation. The merits of two popular estimation methods are discussed, the instrumental variables linear probability model and the bivariate probit, both of which are of interest to applied researchers.  After controlling for unobserved hospital quality and endogenous HIT adoption, my results suggest that HIT adoption significantly reduces a patient's likelihood of dying across all three conditions and the effect size grows when patients are restricted to more homogeneous groups. In Chapter 4, I discuss extensions to the model including the potential impacts of HIT on costs, readmissions, and length of stay for hospital inpatients. The underlying econometric model for cost-side outcomes is likely to be different than the production based approach taken in the previous chapters and deserves special attention. Each of the above chapters and the data used in the analysis are original contributions to this new and growing area of economic research. Chapter 5 concludes the dissertation."},{"label":"dcterms.available","value":"2017-09-20T16:52:38Z"},{"label":"dcterms.contributor","value":"Rizzo, John"},{"label":"dcterms.creator","value":"McKenna, Ryan Michael"},{"label":"dcterms.dateAccepted","value":"2017-09-20T16:52:38Z"},{"label":"dcterms.dateSubmitted","value":"2017-09-20T16:52:38Z"},{"label":"dcterms.description","value":"Department of Economics"},{"label":"dcterms.extent","value":"61 pg."},{"label":"dcterms.format","value":"Monograph"},{"label":"dcterms.identifier","value":"http://hdl.handle.net/11401/77407"},{"label":"dcterms.issued","value":"2016-12-01"},{"label":"dcterms.language","value":"en_US"},{"label":"dcterms.provenance","value":"Made available in DSpace on 2017-09-20T16:52:38Z (GMT). No. of bitstreams: 1\nMcKenna_grad.sunysb_0771E_12834.pdf: 813764 bytes, checksum: 4220037fb65ce9ff7cee645db48789b1 (MD5)\n  Previous issue date:    1"},{"label":"dcterms.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.subject","value":"Economics -- Health sciences"},{"label":"dcterms.title","value":"Essays on the Impact of Health Information Technology on Patient Outcomes"},{"label":"dcterms.type","value":"Dissertation"},{"label":"dc.type","value":"Dissertation"}],"description":"This manifest was generated dynamically","viewingDirection":"left-to-right","sequences":[{"@type":"sc:Sequence","canvases":[{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json","@type":"sc:Canvas","label":"Page 1","height":1650,"width":1275,"images":[{"@type":"oa:Annotation","motivation":"sc:painting","resource":{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/22%2F97%2F92%2F22979206586693524874524149363362574736/full/full/0/default.jpg","@type":"dctypes:Image","format":"image/jpeg","height":1650,"width":1275,"service":{"@context":"http://iiif.io/api/image/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/22%2F97%2F92%2F22979206586693524874524149363362574736","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}