At the Intersection of Health, Health Care and Policy Cite this article as: Jason S. Shapiro, Sarah A. Johnson, John Angiollilo, William Fleischman, Arit Onyile and Gilad Kuperman Health Information Exchange Improves Identification Of Frequent Emergency Department Users Health Affairs, 32, no.12 (2013):2193-2198 doi: 10.1377/hlthaff.2013.0167

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Information Exchange By Jason S. Shapiro, Sarah A. Johnson, John Angiollilo, William Fleischman, Arit Onyile, and Gilad Kuperman

10.1377/hlthaff.2013.0167 HEALTH AFFAIRS 32, NO. 12 (2013): 2193–2198 ©2013 Project HOPE— The People-to-People Health Foundation, Inc.

doi:

Health Information Exchange Improves Identification Of Frequent Emergency Department Users

Jason S. Shapiro (jason [email protected]) is an associate professor and chief of clinical informatics in the Department of Emergency Medicine, Mount Sinai Medical Center, in New York City.

We hypothesized that using communitywide 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 sitespecific 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 < 0:001). 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. ABSTRACT

F

requent users of the emergency department (ED) have been much written about in the medical and health policy literature in the past five to ten years. In the era of health reform, there has been increasing discussion about how to reduce the overuse of emergency services. Some goals of health reform are to reduce overuse of the ED, improve access to a broader range of health services, and potentially improve the overall health of frequent ED users.1 There is no consensus as to who qualifies as a frequent ED user: Definitions in the literature range from people who make at least two ED visits per year to those who make at least five visits per month—a thirtyfold difference.2,3 The most common definition is people who make at least four ED visits per year.4–6 When that definition is used, frequent users are 4.5–8.0 percent of all ED patients but account for 21–28 percent of all ED visits.6 In a study of databases in Massachusetts, Kathleen Fuda and Rachel

Sarah A. Johnson is a medical student at Columbia University, in New York City. John Angiollilo is a medical student at Columbia University. William Fleischman is a resident in the emergency department, Icahn School of Medicine at Mount Sinai, in New York City.

Immekus found that only 1 percent of state residents accounted for 17.6 percent of all ED visits.7 Compared with other ED users, frequent users have more social, psychiatric, and substance abuse issues8–16 and tend to be sicker, with medical conditions that are both more complex and more acute.17–22 In addition, frequent users are more frequently admitted to the hospital,15 incur higher costs,23,24 and have higher mortality rates.25,26 Studies have dispelled the common myth that frequent users are typically uninsured, showing that instead they are more often underinsured participants in government programs such as Medicaid.7,18,20,27,28 The odds ratio in one study was 2.1 (p < 0:001), meaning that publicly insured adults are 2.1 times more likely than privately insured and uninsured adults to be frequent ED users.20 Previous work has shown that patients commonly have “crossover” visits—that is, they visit more than one ED in a single geographic region.29,30 This is especially true for frequent December 2 013

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Arit Onyile is a medical student at Saint George’s University in Grenada. At the time of the study, she was a data analyst in the emergency department at the Icahn School of Medicine at Mount Sinai. Gilad Kuperman is director for interoperability informatics at New York– Presbyterian Hospital, in New York City.

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Information Exchange ED users,7,22,31 which is why the use of data from single institutions limits the ability to effectively identify this cohort for either study purposes or case management interventions.32 Looking at communitywide data sets may give a different picture of frequent ED use. This article describes an analysis of ten EDs at provider organizations that participated in a health information exchange (HIE) in New York City. The goal of the project was to measure the incremental increase in the number of frequent ED users who were identified when data from all of the EDs were compared with sitespecific data.

Study Data And Methods Setting The study sites were the ten hospitals that then participated in the New York Clinical Health Information Exchange (NYCLIX), a regional HIE. All of the hospitals are in the New York City metropolitan area, and all have EDs. Seven of the ten EDs are in Manhattan, two in Brooklyn, and one in Staten Island. The average distance between any two of the sites is 7.2 miles. Exhibit 1 presents some basic statistics about the sites from the New York State Department of Health’s Statewide Planning and Research Cooperative System database.33 According to the

Exhibit 1 Numbers Of Beds, Emergency Department (ED) Visits, And Inpatient Discharges In Ten New York City Hospitals In A Health Information Exchange (HIE) Bedsa

Site

ED visitsb

Inpatient dischargesc

Sites in Manhattan Site Site Site Site Site Site Site

1 2 3 4 5 6 7

871 1,171 814 1,178 879 505 523

64,886 74,287 35,493 114,575 30,584 43,228 61,124

41,186 59,063 45,404 50,675 31,546 29,333 19,903

Sites in Brooklyn or Staten Island Site 8 Site 9 Site 10

714 376 212

60,530 40,366 13,437

41,819 19,057 11,258

7,243

538,510

349,217

All sites HIE

SOURCE Authors’ analysis of data from the sources below. aNew York State Department of Health. New York State hospital profile [Internet]. Albany (NY): The Department; [last modified 2013 Jul 18; cited 2013 Oct 25]. Available from: http://hospitals.nyhealth.gov/index.php?PHPSESSID= 51f042212dbf5db2ec0ffc15401d4779. bNew York State Department of Health. 2006—emergency departments of New York State: county data [Internet]. Albany (NY): The Department; [revised 2008 Oct; cited 2013 Oct 25]. Available from: http://www.health.state.ny.us/statistics/sparcs/ed/2006/. c New York State Department of Health. 2009 annual report: table 8—discharges/average length of stay by county of hospitalization and hospital by service category. Albany (NY): The Department; [revised 2010 Dec; cited 2013 Oct 25]. Available from: http://www.health.ny.gov/statistics/ sparcs/annual/ip/2009/t2009_08.htm.

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same database, NYCLIX hospitals accounted for 76.6 percent of the ED visits in Manhattan, which is where most of the NYCLIX hospitals are located. The NYCLIX HIE provided the technical, organizational, and policy infrastructure to support electronic data exchange across member sites. Two key components of the NYCLIX technical infrastructure made the work done in this study possible. They are the data sent to NYCLIX by participant organizations, which included the date and time of each ED registration, and the NYCLIX master patient index, which links a single patient’s records across multiple institutions.34 This index enabled us to identify a single patient’s encounters with different institutions even though the patient had a different medical record number at each institution. Data Collection And Analysis The NYCLIX staff acted as the “honest broker,” in accordance with the safe harbor rule in the Health Insurance Portability and Accountability Act (HIPAA) of 1996 governing the use of deidentified data in research and with the New York State policy governing the use of HIE data for research. The staff also performed retrospective queries of the NYCLIX clinical data based on our definition of frequent ED users: at least one instance of four or more ED visits within thirty days during the study period, June 1, 2010–May 31, 2011. The staff then provided our research team with aggregate and deidentified data for further analysis. The queries were based on all ED visits that occurred at the ten sites during the study period. Visits occurring within six hours of another ED visit at the same site were excluded based on local expert opinion, and because excluding them biased the results in favor of our null hypothesis. Administrative leaders at several EDs believed that repeat visits to the same site within six hours might disproportionately represent common administrative or clerical errors in recording ED admissions and include instances where patients were electronically discharged prematurely and then reregistered, without actually leaving the ED. For each site and for the HIE as a whole, we calculated the number of ED visits, number of patients accounting for these visits, average number of ED visits per patient during the twelve-month study period, number of patients who were frequent ED users according to our definition, number of ED visits that were accounted for by the frequent users, and average number of visits per frequent user.We calculated the increase in the numbers of frequent users and their ED visits when we used data from across the entire HIE, compared with the num-

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bers when we used site-specific data. For the calculation of observed counts of frequent ED users, a chi-square analysis was performed. For all other analyses, the Wilcoxon signed-rank test was used because the distributions of visit and patient counts were skewed. Alpha was set to 0.05 for all analyses. After reviewing the study protocol, the Icahn School of Medicine at Mount Sinai’s Program for the Protection of Human Subjects determined that this study did not require approval, since it used only deidentified data. Limitations This study has some limitations. First, the primary focus was on data from one HIE in New York City, and our results might not be generalizable to other regions. However, substantial rates of crossover visits have also been reported in other regions. Second, not all hospitals in New York City were participating in the NYCLIX HIE at the time of this study. Notably absent were the eleven public hospitals in the New York City Health and Hospitals Corporation that serve 1.4 million patients every year. If these hospitals—some of which are in close proximity to hospitals that we studied—were included, the expected affect of the HIE would be even greater than we measured. Consolidation of HIEs in the New York City area is taking place. For example, NYCLIX merged with the Long Island Patient Infor-

mation Exchange after this study to form Healthix, a larger HIE. Healthix has announced plans to merge with the Brooklyn Health Information Exchange. Given these changes, one or both of the limitations of this study might not apply if this research is repeated in the future.

Study Results Our queries revealed 924,675 visits by 591,632 unique patients at NYCLIX EDs during our study period.We excluded 4,168 of those visits because they occurred within six hours of a previous ED visit, as explained above. After this exclusion, there were 920,507 ED visits by 591,632 patients during our study period (Exhibit 2). These patients had an average of 1.6 ED visits per year. When we used site-specific data, we identified 4,785 patients who met our definition of a frequent ED user (Exhibit 2). These patients accounted for 45,771 ED visits and had an average of 9.6 visits per year. They represented 0.8 percent of all ED users in the study and accounted for 5.0 percent of ED visits. However, when we used HIE-wide data, we identified 5,756 frequent ED users, who represented 1.0 percent of all ED users (Exhibit 2). This represents a 20.3 percent increase in the number of frequent users identified. These 5,756 frequent users had 53,031 visits to an ED

Exhibit 2 Emergency Department (ED) Users And Their Visits To Ten New York City Hospitals In A Health Information Exchange (HIE), June 1, 2010–May 31, 2011 Frequent ED users All ED users

Increased detection with HIE-wide data of:

Site-specific data

HIE-wide data

Average no. of visits per user

No. of frequent users

Average no. of visits per frequent user

No. of frequent users

Average no. of visits per frequent user

Frequent ED users

Visits by frequent ED users

Sites in Manhattan Site 1 73,153 Site 2 60,926 Site 3 42,450 Site 4 111,980 Site 5 42,600 Site 6 59,299 Site 7 70,907 Sites in Brooklyn or Staten

1.6 1.6 1.3 1.6 1.2 1.4 1.6 Island

821 575 230 1,015 133 515 624

10.5 9.1 7.3 8.3 7.9 8.8 9.9

1,055 807 370 1,307 303 853 989

9.0 7.5 5.7 7.5 5.2 6.8 7.7

28.5% 40.3 60.9 28.8 127.8 65.6 58.5

9.5% 15.8 26.4 16.2 48.9 28.5 22.4

Site 8 Site 9 Site 10

75,872 46,998 26,489

1.5 1.6 1.4

511 422 72

10.3 9.9 7.7

584 462 101

9.6 9.4 6.6

14.3 9.5 40.3

6.5 3.7 20.1

591,632

1.6

4,785

9.6

5,756

9.2

20.3

16.0

Site

All sites HIE

No. of users

SOURCE Authors’ research and analysis. NOTES HIE-wide totals are less than the sum of site-specific counts because some frequent ED users were counted at multiple sites. Differences between site-specific and HIE-wide data were significant (p < 0:001 except for site 10, site-specific data, average number of visits per frequent user, where p < 0:10).

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Information Exchange in our study (6.0 percent of all ED visits). Thus, using HIE-wide data produced a 16.0 percent increase in the number of ED visits by frequent users that could be identified. Frequent ED users differed from all ED users in several ways. The frequent users were more likely to be male (51.1 percent versus 44.9 percent), their mean age was higher (40.7 years versus 37.9 years), and more of them had crossover visits (28.8 percent versus 3.0 percent). All of these differences were significant (p < 0:001).

Discussion Frequent ED use is a complex issue. There are no standard measures,32 and it is unclear what constitutes excessive use.35 Nonetheless, it is clear that ED use consumes a great amount of health care resources, and it is imperative to manage that use effectively. In addition, the increasing importance of the ED in care coordination models demands that ED use be well understood.36 As sites of unplanned care, EDs may particularly benefit from the exchange of health information.37–40 Although challenges to its use remain,41,42 health information exchange is in continuous use across multiple EDs in New York City. This study demonstrates that using HIE data to obtain a communitywide perspective on frequent ED use gives substantially different results than a site-specific perspective, permitting the identification of 16 percent more visits and 20 percent more frequent users (Exhibit 2). The communitywide perspective more closely represents the patient’s true experience and so may be more suitable as a focus for measurement and quality improvement opportunities. The National Quality Forum’s list of potential 2013 e-quality measures43—quality measures that can be applied to electronic data sources— included preventable ED visits. However, the forum did not propose a specific measure of frequent ED users or their visits, and the general focus of this list was on taking advantage of data from electronic health record systems, rather than of communitywide clinical data sets from an HIE. When other potential quality measures are proposed in the future, a communitywide perspective and the use of an HIE as a data source might be more appropriate in some cases. In addition to improving measurement, an HIE infrastructure can be useful in quality improvement. Previous work has shown that case managers can provide support services that decrease recidivism by frequent ED users.3,44–47 However, the identification of patients suitable for case management is labor intensive and may not be cost-effective.48 Electronic health record systems can be used 2196

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to make the identification of frequent ED users for case management more efficient and reliable.49,50 Using a communitywide HIE might increase the sensitivity of detection. Furthermore, an HIE infrastructure could be used to notify a case manager in real time when a patient registers for his or her fourth ED visit within thirty days anywhere across the entire HIE, rather than in just a single hospital.34 The use of communitywide HIE data for quality improvement need not be limited to frequent ED users. Other potential uses of an HIE infrastructure include notifying primary care providers when their patients visit an ED or are admitted to or discharged from an acute care hospital,34,51 detecting returns within seventy-two hours to an ED across multiple acute care hospitals,52 detecting readmissions within thirty days across multiple hospitals,53 and alerting providers at the point of test order entry (for example, for computed tomography scans) if a patient has had multiple similar studies previously.54 Claims data or other large clinical data sets may be available for the type of retrospective analysis presented in this study. However, they often have long lag times, making them unsuitable data sources for quality improvement interventions that require real-time data for notification services.33 Some data sets (for example, the National Hospital Ambulatory Medical Care Survey) also use sampling techniques or lack clinical details (as is the case with claims data), which makes them unsuitable for tracking individual patients. From a policy perspective, designers of innovative health care delivery models, such as Medicaid’s health home program, that are intended to improve quality may want to consider the inclusion of communitywide data as part of their measurement program. In New York State, participation in the health home program requires that provider organizations be part of an HIE, which can facilitate communitywide quality measurement. Accountable models of care may also have incentives to incorporate communitywide data into quality measurement. Despite the potential benefits of health information exchange, there are barriers to its broad use for measurement and quality improvement. The financial sustainability of regional health information organizations, which create much of the HIE infrastructure across the country, is far from secure.55 In addition, some models for health information exchange focus on transmitting information only when it is needed and do not aggregate a patient’s data.56 Such models might not support communitywide measurement or quality improvement uses. The full use of health information exchange

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for measurement and quality improvement would require consideration of privacy and data use policies. Some states have policies and laws that require clinicians to obtain written consent from the patient before clinical information can be accessed via an HIE.57 Many policies that allow a clinician to be automatically notified based on data that reside in an HIE are not explicitly stated.58 Policies regarding the use of HIE data should be clarified in a way that protects patients’ privacy while allowing data to be used for measurement and quality improvement. Jason Shapiro was supported in part by the Agency for Healthcare Research and Quality (Grant No. R01HS021261). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Gilad Kuperman was the executive director of the New York Clinical Health Information

Conclusion Our study demonstrates that a communitywide examination of the population and its sources of care, which is more representative of patients’ true experiences in seeking care, provides different results for frequent ED use—an important quality measure—than does a site-specific analysis. Health information exchange holds important promise for novel approaches to measuring and improving quality. However, important technical and policy issues remain to be resolved before its full potential can be realized. ▪

Exchange (NYCLIX), and Shapiro received some research funding directly from NYCLIX before this study was conducted. At the time of this study, NYCLIX was an independent health information exchange. It has since merged with the Long Island Patient Information Exchange to form a new organization, Healthix Inc. Another organization, the New York eHealth

Collaborative (NYeC), a public-private partnership, now works with Healthix to provide all data and technical infrastructure. The authors thank Laurence Berg, Tina Lowry, and other technical staff members from NYeC and Healthix who provided deidentified data to the research team for the analyses performed.

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Health information exchange improves identification of frequent emergency department users.

We hypothesized that using communitywide data from a health information exchange (HIE) could improve the ability to identify frequent emergency depart...
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