Tina Lowry

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

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.

The visit-data warehouse: enabling novel secondary use of health information exchange data.

Introduction/Objectives: Health Information Exchange (HIE) efforts face challenges with data quality and performance, and this becomes especially problematic when data is leveraged for uses beyond primary clinical use. We describe a secondary data infrastructure focusing on patient-encounter, nonclinical data that was built on top of a functioning HIE platform to support novel secondary data uses and prevent potentially negative impacts these uses might have otherwise had on HIE system performance. Background: HIE efforts have generally formed for the primary clinical use of individual clinical providers searching for data on individual patients under their care, but many secondary uses have been proposed and are being piloted to support care management, quality improvement, and public health. Description of the HIE and Base Infrastructure: This infrastructure review describes a module built into the Healthix HIE. Healthix, based in the New York metropolitan region, comprises 107 participating organizations with 29,946 acute-care beds in 383 facilities, and includes more than 9.2 million unique patients. The primary infrastructure is based on the InterSystems proprietary Caché data model distributed across servers in multiple locations, and uses a master patient index to link individual patients’ records across multiple sites. We built a parallel platform, the “visit data warehouse,” of patient encounter data (demographics, date, time, and type of visit) using a relational database model to allow accessibility using standard database tools and flexibility for developing secondary data use cases. These four secondary use cases include the following: (1) tracking encounter-based metrics in a newly established geriatric emergency department (ED), (2) creating a dashboard to provide a visual display as well as a tabular output of near-real-time de-identified encounter data from the data warehouse, (3) tracking frequent ED users as part of a regional-approach to case management intervention, and (4) improving an existing quality improvement program that analyzes patients with return visits to EDs within 72 hours of discharge. Results/Lessons Learned: Setting up a separate, near-real-time, encounters-based relational database to complement an HIE built on a hierarchical database is feasible, and may be necessary to support many secondary uses of HIE data. As of November 2014, the visit-data warehouse (VDW) built by Healthix is undergoing technical validation testing and updates on an hourly basis. We had to address data integrity issues with both nonstandard and missing HL7 messages because of varied HL7 implementation across the HIE. Also, given our HIEs federated structure, some sites expressed concerns regarding data centralization for the VDW. An established and stable HIE governance structure was critical in overcoming this initial reluctance. Conclusions: As secondary use of HIE data becomes more prevalent, it may be increasingly necessary to build separate infrastructure to support secondary use without compromising performance. More research is needed to determine optimal ways of building such infrastructure and validating its use for secondary purposes.

Bouncing Back Elsewhere: Multilevel Analysis of Return Visits to the Same or a Different Hospital After Initial Emergency Department Presentation

Study objective Analyses of 72‐hour emergency department (ED) return visits are frequently used for quality assurance purposes and have been proposed as a means of measuring provider performance. These analyses have traditionally examined only patients returning to the same hospital as the initial visit. We use a health information exchange network to describe differences between ED visits resulting in 72‐hour revisits to the same hospital and those resulting in revisits to a different site. Methods We examined data from a 31‐hospital health information exchange of all ED visits during a 5‐year period to identify 72‐hour return visits and collected available encounter, patient, and hospital variables. Next, we used multilevel analysis of encounter‐level, patient‐level, and hospital‐level data to describe differences between initial ED visits resulting in different‐site and same‐site return visits. Results We identified 12,621,159 patient visits to the 31 study EDs, including 841,259 same‐site and 107,713 different‐site return visits within 72 hours of initial ED presentation. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) for the initial‐visit characteristics’ predictive relationship that any return visit would be at a different site: daytime visit (OR 1.10; 95% CI 1.07 to 1.12), patient‐hospital county concordance (OR 1.40; 95% CI 1.36 to 1.44), male sex (OR 1.27; 95% CI 1.24 to 1.30), aged 65 years or older (OR 0.55; 95% CI 0.53 to 0.57), sites with an ED residency (OR 0.41; 95% CI 0.40 to 0.43), sites at an academic hospital (OR 1.12; 95% CI 1.08 to 1.15), sites with high density of surrounding EDs (OR 1.73; 95% CI 1.68 to 1.77), and sites with a high frequency of same‐site return visits (OR 0.10; 95% CI 0.10 to 0.11). Conclusion This analysis describes how ED encounters with early revisits to the same hospital differ from those with revisits to a second hospital. These findings challenge the use of single‐site return‐visit frequency as a quality measure, and, more constructively, describe how hospitals can use health information exchange to more accurately identify early ED return visits and to support programs related to these revisits.

Expanding Health Information Exchange Improves Identification of Frequent Emergency Department Users

Study objective Frequent emergency department (ED) users are of interest to policymakers and hospitals. The objective of this study is to examine the effect of health information exchange size on the identification of frequent ED users. Methods We retrospectively analyzed data from Healthix, a health information exchange in New York that previously included 10 hospitals and then grew to 31 hospitals. We divided patients into 3 cohorts: high‐frequency ED users with 4 or more visits in any 30‐day period, medium‐frequency ED users with 4 or more visits in any year, and infrequent ED users with fewer than 4 visits in any year. For both the smaller (10‐hospital) and larger (31‐hospital) health information exchanges, we compared the identification rate of frequent ED users that was based on hospital‐specific data with the corresponding rates that were based on health information exchange data. Results The smaller health information exchange (n=1,696,279 unique ED patients) identified 11.4% more high‐frequency users (33,467 versus 30,057) and 9.5% more medium‐frequency users (109,497 versus 100,014) than the hospital‐specific data. The larger health information exchange (n=3,684,999) identified 19.6% more high‐frequency patients (52,727 versus 44,079) and 18.2% more medium‐frequency patients (222,574 versus 192,541) than the hospital‐specific data. Expanding from the smaller health information exchange to the larger one, we found an absolute increase of 8.2% and 8.7% identified high‐ and medium‐frequency users, respectively. Conclusion Increasing health information exchange size more accurately reflects how patients access EDs and ultimately improves not only the total number of identified frequent ED users but also their identification rate.