scholarly journals Identifying homelessness using health information exchange data

2015 ◽  
Vol 22 (3) ◽  
pp. 682-687 ◽  
Author(s):  
John Zech ◽  
Gregg Husk ◽  
Thomas Moore ◽  
Gilad J Kuperman ◽  
Jason S Shapiro
Keyword(s):  
New York ◽  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Housing Status ◽  
Number Of Patients ◽  
Address Data ◽  
Patients Experience ◽  
Exchange Data

Abstract Background Homeless patients experience poor health outcomes and consume a disproportionate amount of health care resources compared with domiciled patients. There is increasing interest in the federal government in providing care coordination for homeless patients, which will require a systematic way of identifying these individuals. Objective We analyzed address data from Healthix, a New York City–based health information exchange, to identify patterns that could indicate homelessness. Methods Patients were categorized as likely to be homeless if they registered with the address of a hospital, homeless shelter, place of worship, or an address containing a keyword synonymous with “homelessness.” Results We identified 78 460 out of 7 854 927 Healthix patients (1%) as likely to have been homeless over the study period of September 30, 2008 to July 19, 2013. We found that registration practices for these patients varied widely across sites. Conclusions The use of health information exchange data enabled us to identify a large number of patients likely to be homeless and to observe the wide variation in registration practices for homeless patients within and across sites. Consideration of these results may suggest a way to improve the quality of record matching for homeless patients. Validation of these results is necessary to confirm the homeless status of identified individuals. Ultimately, creating a standardized and structured field to record a patient’s housing status may be a preferable approach.

scholarly journals Patient crossover and potentially avoidable repeat computed tomography exams across a health information exchange

2016 ◽  
Vol 24 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Benjamin H Slovis ◽  
Tina Lowry ◽  
Bradley N Delman ◽  
Anton Oscar Beitia ◽  
Gilad Kuperman ◽  
...  
Keyword(s):  
Computed Tomography ◽  
New York ◽  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Cohort Analysis ◽  
Time Frame ◽  
Ct Scans ◽  
Number Of Patients ◽  
Alerting System

Objective: The purpose of this study was to measure the number of repeat computed tomography (CT) scans performed across an established health information exchange (HIE) in New York City. The long-term objective is to build an HIE-based duplicate CT alerting system to reduce potentially avoidable duplicate CTs. Methods: This retrospective cohort analysis was based on HIE CT study records performed between March 2009 and July 2012. The number of CTs performed, the total number of patients receiving CTs, and the hospital locations where CTs were performed for each unique patient were calculated. Using a previously described process established by one of the authors, hospital-specific proprietary CT codes were mapped to the Logical Observation Identifiers Names and Codes (LOINC®) standard terminology for inter-site comparison. The number of locations where there was a repeated CT performed with the same LOINC code was then calculated for each unique patient. Results: There were 717 231 CTs performed on 349 321 patients. Of these patients, 339 821 had all of their imaging studies performed at a single location, accounting for 668 938 CTs. Of these, 9500 patients had 48 293 CTs performed at more than one location. Of these, 6284 patients had 24 978 CTs with the same LOINC code performed at multiple locations. The median time between studies with the same LOINC code was 232 days (range of 0 to 1227); however, 1327 were performed within 7 days and 5000 within 30 days. Conclusions: A small proportion (3%) of our cohort had CTs performed at more than one location, however this represents a large number of scans (48 293). A noteworthy portion of these CTs (51.7%) shared the same LOINC code and may represent potentially avoidable studies, especially those done within a short time frame. This represents an addressable issue, and future HIE-based alerts could be utilized to reduce potentially avoidable CT scans.

scholarly journals An empirical analysis of the financial benefits of health information exchange in emergency departments

2015 ◽  
Vol 22 (6) ◽  
pp. 1169-1172 ◽  
Author(s):  
Niam Yaraghi
Keyword(s):  
New York ◽  
Health Information ◽  
Information Exchange ◽  
Laboratory Tests ◽  
Negative Binomial ◽  
Health Information Exchange ◽  
Negative Binomial Regression ◽  
Control Group ◽  
History Of ◽  
The Impact

Abstract Objective To examine the impact of health information exchange (HIE) on reducing laboratory tests and radiology examinations performed in an emergency department (ED). Materials and Methods The study was conducted in an ED setting in Western New York over a period of 2 months. The care of the patients in the treatment group included an HIE query for every encounter, while the care of other patients in the control group did not include such queries. A group of medical liaisons were hired to query the medical history of patients from an HIE and provide it to the ED clinicians. Negative binomial regression was used to analyze the effects of HIE queries on the number of performed laboratory tests and radiology examinations. The log files of the HIE system since 1 year before the ED admission were used to analyze the differences in outcome measures between the 2 groups of patients. Results Ceteris paribus, HIE usage is associated with, respectively, 52% and 36% reduction in the expected total number of laboratory tests and radiology examinations ordered per patient at the ED. Conclusions The results indicate that access to additional clinical data through the HIE will significantly reduce the number of laboratory tests and radiology examinations performed in the ED settings and thus support the ongoing HIE efforts.

scholarly journals Evaluation of a health information exchange system for microcephaly case-finding — New York City, 2013—2015

PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0237392
Author(s):  
Eugenie Poirot ◽  
Carrie W. Mills ◽  
Andrew D. Fair ◽  
Krishika A. Graham ◽  
Emily Martinez ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Health Information ◽  
York City ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Case Finding ◽  
Exchange System

scholarly journals Drivers of information disclosure on health information exchange platforms: insights from an exploratory empirical study

2015 ◽  
Vol 22 (6) ◽  
pp. 1183-1186 ◽  
Author(s):  
Niam Yaraghi ◽  
Raj Sharman ◽  
Ram Gopal ◽  
Ranjit Singh ◽  
R Ramesh
Keyword(s):  
Primary Care ◽  
New York ◽  
Health Information ◽  
Information Exchange ◽  
Information Disclosure ◽  
Primary Care Physicians ◽  
Medical Information ◽  
Health Information Exchange ◽  
Care Physician ◽  
Willingness To Disclose

Abstract Objective The objective of this research is to empirically explore the drivers of patients’ consent to sharing of their medical records on health information exchange (HIE) platforms. Materials and Methods The authors analyze a dataset consisting of consent choices of 20 076 patients in Western New York. A logistic regression is applied to empirically investigate the effects of patients’ age, gender, complexity of medical conditions, and the role of primary care physicians on patients’ willingness to disclose medical information on HIE platforms. Results The likelihood of providing consent increases by age (odds ratio (OR) = 1.055; P  < .0001). Female patients are more likely to provide consent (OR = 1.460; P  = .0003). As the number of different physicians involved in the care of the patient increases, the odds of providing consent slightly increases (OR = 1.024; P  = .0031). The odds of providing consent is significantly higher for the patients whom a primary care physician has been involved in their medical care (OR = 1.323; P  < .0001). Conclusion Individual-level characteristics are important predictors of patients’ willingness to disclose their medical information on HIE platforms.

Validating Health Information Exchange Data for Quality Measurement

2013 ◽  
Vol 62 (4) ◽  
pp. S94
Author(s):  
J.S. Shapiro ◽  
A. Onyile ◽  
N. Genes ◽  
C. DiMaggio ◽  
G. Kuperman ◽  
...  
Keyword(s):  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Quality Measurement ◽  
Exchange Data
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