Research Studies

Research Studies Conducted Using Healthix Data

The studies are listed in date order from most recent to oldest. They do not reflect all research conducted, but rather a sampling of the breadth of topics studied.

 

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

May 2016
Slovis, Benjamin & Lowry, Tina & Delman, Bradley & Oscar Beitia, Anton & Kuperman, Gilad & Dimaggio, Charles & Shapiro, Jason.

ABSTRACT

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.


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Identifying homelessness using health information exchange data

February 2015
Zech, John & Husk, Gregg & Moore, Thomas & J Kuperman, Gilad & Shapiro, Jason

ABSTRACT

We analyzed address data from Healthix, a New York City-based health information exchange, to identify patterns that could indicate homelessness.
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.”

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.

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. https://www.ncbi.nlm.nih.gov/pubmed/25670759


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Health Information Exchange Improves Identification Of Frequent Emergency Department Users

December 2013
Shapiro, Jason & A Johnson, Sarah & Angiollilo, John & Fleischman, William & Onyile, Arit & Kuperman, Gilad

ABSTRACT

We hypothesized that using community-wide data from a health information exchange (HIE) could improve the ability to identify frequent emergency department (ED) users-those with four or more ED visits in thirty days-by allowing ED use to be measured across unaffiliated hospitals. When we analyzed HIE-wide data instead of site-specific data, we identified 20.3 percent more frequent ED users (5,756 versus 4,785) and 16.0 percent more visits by them to the ED (53,031 versus 45,771). Additionally, we found that 28.8 percent of frequent ED users visited multiple EDs during the twelve-month study period, versus 3.0 percent of all ED users. All three differences were significant ($$p ). An improved ability to identify frequent ED users allows better targeting of case management and other services that can improve frequent ED users’ health and reduce their use of costly emergency medical services. https://www.ncbi.nlm.nih.gov/pubmed/24301405


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Event Detection: A Clinical Notification Service on a Health Information Exchange Platform

November 2012
Shapiro, Jason & Thomas Moore & Luke Doles & Neil Calman & Eli Camhi & Thomas Check & Arit Onyile &Kuperman, Gilad

ABSTRACT

Notifying ambulatory providers when their patients visit the hospital is a simple concept but potentially a powerful tool for improving care coordination. A health information exchange (HIE) can provide automatic notifications to its members by building services on top of their existing infrastructure. NYCLIX, Inc., a functioning HIE in New York City, has developed a system that detects hospital admissions, discharges and emergency department visits and notifies their providers. The system has been in use since November 2010. Out of 63,305 patients enrolled 6,913 (11%) had one or more events in the study period and on average there were 238 events per day. While event notifications have a clinical value, their use also involves non-clinical care coordination; new workflows should be designed to incorporate a broader care team in their use. This paper describes the user requirements for the notification system, system design, current status, lessons learned and future directions. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540505/