Potential Value of Health Information Exchange for People with Epilepsy: Crossover Patterns and Missing Clinical Data Zachary M Grinspan, MD MS1,2,3,4, Erika L Abramson, MD MS1,2,3,4,5, Samprit Banerjee, PhD2,6, Lisa M Kern, MD MPH1,2,4,5,7, Rainu Kaushal, MD MPH1,2,3,4,5,7, Jason S Shapiro, MD MA8 1

Center for Healthcare Informatics and Policy, Weill Cornell Medical College, New York, NY Department of Public Health, Weill Cornell Medical College, New York, NY 3 Department of Pediatrics, Weill Cornell Medical College, New York, NY 4 New York Presbyterian Hospital, New York, NY 5 Health Information Technology Evaluation Collaborative, New York, NY 6 Department of Statistical Science, Cornell University, Ithaca, NY 7 Department of Medicine, Weill Cornell Medical College, New York, NY 8 Department of Emergency Medicine, Mount Sinai Medical Center, New York, NY 2

ABSTRACT Context: For people with epilepsy, the potential value of health information exchange (HIE) is unknown. Methods: We reviewed two years of clinical encounters for 8055 people with epilepsy from seven Manhattan hospitals. We created network graphs illustrating crossover among these hospitals for multiple encounter types, and calculated a novel metric of care fragmentation: “encounters at risk for missing clinical data.” Results: Given two hospitals, a median of 109 [range 46 – 588] patients with epilepsy had visited both. Due to this crossover, recent, relevant clinical data may be missing at the time of care frequently (44.8% of ED encounters, 34.5% inpatient, 24.9% outpatient, and 23.2% radiology). Though a smaller percentage of outpatient encounters were at risk for missing data than ED encounters, the absolute number of outpatient encounters at risk was three times higher (14,579 vs. 5041). Conclusion: People with epilepsy may benefit from HIE. Future HIE initiatives should prioritize outpatient access. INTRODUCTION Epilepsy is a common chronic neurologic condition affecting 2.2 million people in the US.1 Compared to the general population, people with epilepsy are at higher risk for injury,2 progressive cognitive impairment,3, 4 depression,5 suicidality,6 social isolation,7 decreased quality of life,8 unemployment,9 and early death.10-12 Epilepsy is a chronic ambulatory care sensitive condition, i.e. high quality outpatient care can reduce the need for inpatient and emergency department (ED) care.13-16 “Care continuity” is a key component of high quality outpatient care. Care continuity can be conceptually divided into longitudinal (same institution), interpersonal (same provider), and informational continuity (complete medical record).17 Thus a person’s care is “continuous” if decisions made by the person’s current care provider reflects an ongoing, personal, and well informed relationship between the person and the health system.17 Better care continuity leads to better health outcomes for patients with ambulatory care sensitive conditions, including epilepsy.18, 19 Health information exchange (HIE) connects electronic health record systems across multiple institutions, allowing physicians to share clinical data. This technology may improve care quality,20-22 increase patient satisfaction,23 lower health care costs,24-28 reduce medical errors,29 and improve population health.30, 31 Health information exchange is explicitly designed to improve informational care continuity,32 and therefore may improve health outcomes for people with ambulatory care sensitive conditions. However, the potential value of HIE for specific diseases, such as epilepsy, is underexplored.

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A common approach to measure the potential value of HIE is to estimate how often patients visit clinicians who do not share electronic health record systems (EHRs).33-36 However, there is significant variability in how these measurements are conceptualized and implemented, particularly in the following three areas. First, studies use heterogeneous terminology. Terms like “information gaps”33, 37 and “missing clinical information”38 refer to the absence of important data at the time of care, whereas a term like “crossover”39, 40 refers to the act of seeking care from providers at different hospitals. Other terms like “medical information fragmentation”,34 “care fragmentation”,35 and “the scope of care coordination”36 are less specific, and evoke both the pattern of care and the missing data. In this paper, we will use the term “crossover” to describe the act of seeking care from providers at different hospitals, and “missing clinical data” to describe the consequent inaccessibility of medical information at the time of care. Second, different authors use different methods to quantify the fragmentation of medical information. Encounter based surveys and chart reviews tend to cite the number of visits known (or suspected) to be missing important clinical data as a percentage of all the visits studied.33, 37, 41 However, large database studies tend to estimate the frequency of missing data by calculating the percentage of all encounters accounted for by people who visit multiple sites.34, 39 These two measurements are different. If an otherwise healthy patient visits two different hospitals, the encounter-based method would say only the second visit was missing clinical information, whereas the largedatabase method would count both visits. Additionally, the database studies do not attempt to establish when missing clinical data is recent or relevant. Third, studies measure missing data over different time periods, from a few weeks37 to five years,34 further complicating comparison. We previously described crossover among patients with epilepsy, and showed that 22% of people with epilepsy who seek care at one of seven hospitals in New York City also seek care at others.35 However, our initial work did not include an analysis of the pattern of crossover, nor did it explore the potential consequences of crossover. In this study, we extend our initial description of crossover in three important ways. First, in order to identify the pattern of crossover for people with epilepsy within this group of hospitals, we draw and analyze network graphs, which illustrate the number of people seen at each pair of hospitals. Second, in order to quantify the potential negative impact of crossover, we define and calculate a novel measurement called “encounters at risk for missing clinical data”. We focus our measurements on data that is both recent and relevant. Third, in order to understand which clinical settings have the most need for HIE, we perform the above calculations for outpatient, inpatient, ED, and radiology encounters. We also examine crossover for specific head imaging studies commonly performed on people with epilepsy. METHODS Study Design. We used a cross-sectional study design to describe crossover patterns across seven Manhattan hospitals in several settings for a group of 8055 people with epilepsy. We based our calculations on the visit history for each patient over two full years. The Weill Cornell Institutional Review Board exempted from review this analysis of deidentified data. Setting / Data Source. NYCLIX (New York CLinical Information eXchange) was a regional health information exchange network in New York, New York that collected clinical data from several hospitals in the New York City area.† For this study, NYCLIX provided a de-identified, patient-level data set containing demographics, encounter dates, and ICD9 codes for outpatient, ED, inpatient, and radiology encounters from seven hospitals. Encounter dates were de-identified by a date shifting algorithm that preserved time between events.42 Although NYCLIX actively collected data, clinical

                                                                                                                †

At the time of this study, NYCLIX was an independent health information exchange. As of April 2012, NYCLIX merged with the Long-Island based RHIO Lipix to form a new organization, Healthix Inc.

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access was limited to a small group of pilot users at three sites. Thus, during the study period, the providers at these seven hospitals did not use NYCLIX, except for a negligible fraction of encounters. Inclusion Criteria. We defined a person with epilepsy via a published consensus definition of “probable epilepsy”,43 based on ICD-9 codes: one encounter with an ICD-9 code of 345.x (epilepsy) OR two encounters on separate days with an ICD-9 code of 780.39 (convulsions). We included 8055 people who met this definition between March 2009 and February 2010. Network Graphs (Patient-level analysis). We performed a patient-level analysis of visit patterns by plotting network graphs to visualize the patterns of crossover. To do so, we created a bipartite graph of patients and hospitals, then collapsed it into a unipartite graph showing only the relationship between hospitals.44 (Figure 1) The thickness and shading of the lines connecting two hospitals visually represents the relative number of patients who visited both hospitals. We operationalized the idea of the network “connectedness” by calculating the proportion of nonzero pairwise connections (also called the “density” of the network). We plotted the network graph and calculated the network connectedness for eight encounter types: 1) all visits, 2) emergency department visits, 3) outpatient visits, 4) inpatient visits, and radiology encounters with any of the following: 5) any study, 6) any brain imaging study, 7) head CTs, and 8) brain MRIs. In order to quantify the amount of crossover on average between pairs of hospitals, we calculated the median and range of the number of patients who visited both hospitals across all 21 possible pairs, for each of these visit types. We refer to this measurement as the “average pairwise crossover” of patients between hospitals. Encounters At Risk for Missing Clinical Data (Encounter-level analysis). We performed an encounter-level analysis by counting the number of encounters at risk for missing clinical data. We operationalized the concept of “at risk for missing clinical data” in two ways, one for clinical encounters, and another for radiology encounters. The calculations estimate how often there is recent, relevant clinical data that a physician might use to assist clinical interpretation and decision-making. Thus the number of visits at risk for missing clinical data will be greater than the number of visits in which a physician actually desired unavailable information. The measurements are useful to quantify such risk, as well as to compare the risk across different care settings. For clinical encounters (inpatient, outpatient, and ED), we presumed the treating physician might need any clinical data collected in the past year. Thus we calculated the percentage of encounters in which the patient had visited a different hospital for any reason in the past 1 year. For radiology encounters, radiologists’ interpretations of images are often improved by comparison to previous studies; however, a radiologist may be appropriately less interested in the details of patient’s previous ED, inpatient, or outpatient encounters. Thus we calculated the percentage of radiology encounters in which the patient had any previous radiology encounter at a different hospital within the past year. For radiology encounters consisting of any brain imaging, head CTs, or brain MRIs, the encounter was only at risk if there was a previous study of the same type at a different hospital within the past year. In our enumeration of encounters at risk for missing clinical data, we only counted encounters from the second year of each patient’s two-year visit history, to insure there was always a one-year look back period. We wanted to understand how these measurements might vary from year to year, given a similar population of people with epilepsy with similar patterns of care. Thus we estimated the variability of the encounters at risk for missing clinical data by using bootstrap percentiles to construct confidence intervals.45 To do so, we created a bootstrap sample by randomly selecting people in the cohort with replacement until the bootstrap sample was the same size as the original sample. We then calculated the percent of encounters at risk for missing clinical data in the bootstrap sample. We repeated this process on 1000 bootstrap samples, creating 1000 measurements of encounters at risk. We interpreted the range of values between the 2.5% and 97.5% quantiles as a 95% confidence interval.

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Statistical Tools. Statistical analysis was performed using the R software package.46 We used the “plyr” and “data.table” packages for data aggregation, and the “igraph” package to draw the network graphs.47-49

A"

Hospital A Hospital B Hospital C Hospital D D Hospital A Hospital B Hospital C Hospital AaronAaron • • BettyBetty Charles • • • Charles • • • Dina Dina • • • • • • • • Elias Elias • • • • • • FionaFiona • • • •

B"

1 0 0 Patient-Provider Patient-Hospital MatrixMatrix: (Bipartite):! Patient-Provider Matrix: 1 0 1

01 00 10 11 10 01

00 00 11 11 11 00

00 00 11 11 11 10

0 0 1 1 1 1

Transpose Patient-Provider Matrix x Patient-Provider Matrix = Provider Graph Matrix Transpose Patient-Provider x Patient-Provider = Provider Matrix C" Transpose Patient-Hospital Matrix xMatrix Patient-Hospital Matrix Matrix = Hospital MatrixGraph (Unipartite)! 1 01 0 00 0 00 0 00

00 10 10 10

10 11 11 11

01 11 11 11

1 10 10 01 00 01 01 11 10 1

00 00 11 11 11 00

00 00 11 11 11 10

0 3 0 3 1 ! 1 ! 3 = = ! 1 ! ! ! 1 ! ! 1

E"

D"

A B B 1 Hospitals Hospitals C C 1 D D 2

!

01 00 10 11 10 01

Hospitals Hospitals A B B C C 1 1 3 3 2 3 3 3 3

1 1 3 3 3 ! ! !

21 33 33 4 !

2 3 3 4

BB

CC # of Patients # of Patients 1 1 2 2 3 3

AA

DD

!

Figure 1. Technique to create hospital networks. In this simplified example, imagine a network of four hospitals (A – D) visited by six patients. Panel A. List the patients and mark which hospitals each has visited. Panel B. Turn the list into a “patient-hospital” matrix, with four columns (one for each hospital) and enough rows for each patient. “1” indicates a patient visited that hospital; otherwise enter “0”. Panel C. Multiply the patient-hospital matrix by its transpose to create a hospital network matrix. The rows and columns each correspond to the list of hospitals. Each off-diagonal cell (black) represents the number of patients who visited both the row hospital and the column hospital. Panel D. These values can be tabulated to show the number of patients who visited pairs of hospitals. Panel E. Plot the values as a network graph. Each vertex represents a hospital, and the thickness of the connection between each pair scales with the number of people. In the language of graph theory, this is mathematically equivalent to transforming a bipartite graph (patients and hospitals) into a unipartite graph (hospitals).44

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RESULTS # of Patients

All Visits

10 20 50 100 200

Emergency Department

C

Inpatient

C B

C B

D

C B

D

D

A

A

E

E

G

A E

G

F

B

D

A E

Outpatient

G

F

G

F

F

100% connected 109 [45 - 588]† patients shared per connection

100% connected 42 [17 - 168]† patients shared per connection

100% connected 24 [10 - 99]† patients shared per connection

100% connected 24 [3 - 367]† patients shared per connection

Any Radiology

Any Brain Imaging

CT Head

MRI Brain

C

C B

C B

D

D

D

A

E

A

E

E

G

A E

G

F

B

D

A

100% connected 42 [23 - 162]† patients shared per connection

C B

G

F

F

100% connected 13 [8 - 43]† patients shared per connection

100% connected 9 [2 - 36]† patients shared per connection

G F 81% connected 2 [0 - 7]† patients shared per connection

† median [range]!

Figure 2. Hospital Network Graphs Illustrating Crossover Patterns for 8055 People with Epilepsy for Eight Encounter Types Over Two Years. Each circle represents one of seven Manhattan hospitals. The thickness of the line connecting two circles represents the number of people with epilepsy who were seen at both hospitals, for the given encounter type, over two years. The “All Visits” graph includes any clinical or radiology encounter. The other seven graphs only include encounters of the given type. The connectedness of each graph (i.e. the network density) represents the percent of all hospital pairs that shared patients. For each graph, the median and range of patients shared across all 21 hospital pairs are also presented. Pattern of Patient Crossover People with epilepsy crossed over all (100%) of 21 possible pairs of hospitals in the ED, inpatient, outpatient, and radiology settings, as well as for any brain imaging, and head CTs. People with epilepsy crossed over 81% of possible pairs for brain MRIs. Our overall calculation of the average pairwise crossover found a median of 109 [range 46 to 588] people with epilepsy visited any given pair of hospitals. The average pairwise crossover in the ED (42 [17 – 168]) and radiology (42 [23 – 162]) settings were similar to each other. Although the median crossover in the outpatient setting was lower, the range was substantial (24 [3 – 367]) indicating that a few select pairs of hospitals had high crossover rates. Average pairwise crossover was also measured for inpatient care (24 [10 – 99]) and for specific imaging studies, any brain imaging (13 [8 – 43]), head CTs (9 [2 – 36]), and brain MRIs (2 [0 – 7]).

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TABLE 1. Encounters at Risk for Missing Clinical Data, Among 8055 People with Epilepsy, Over 1 Year. C.###Encounters#with# a#Relevant#Prior# Encounter#within# the#Past#1#Year#at#a# Different#Hospital

C#/#A#=#Percent#of# Encounters#at#Risk#for# Missing#Data [Bootstrap#95%#CI]

21602

28.6%&[27.9&*&32.4]

5041

44.8%&[39.8&*&50.4]

1982

34.5%&[31.3&*&37.8]

58498

Any$clinical$or$ radiology$encounter Any$clinical$or$ radiology$encounter Any$clinical$or$ radiology$encounter Any$clinical$or$ radiology$encounter

14579

24.9%&[22.6&*&27.4]

11000

Any$Radiology$Study

2555

23.2%&[20&*&26.5]

Any$Brain$Image

2627

Any$Brain$Image

381

14.5%&[11.5&*&18]

CT$Head

1757

CT$Head

256

14.6%&[11.1&*&18.4]

MRI$Brain

686

MRI$Brain

27

3.9%&[1.6&*&6.9]

A.#Number#of# Encounters

B.#Definition#of# "Relevent#Prior# Encounter"

CLINICAL&ENCOUNTERS Any$Encounter$(ED,$Inpatient,$ or$Outpatient)

75508

ED

11258

Inpatient Outpatient

5752

RADIOLOGY&ENCOUNTERS Any$Radiology$Study

Encounters at Risk for Missing Data More than a quarter of clinical encounters (28.6% [27.9 – 32.4]) and nearly a quarter of radiology encounters (23.2% [20.0 – 26.5]) were at risk for missing clinical data, i.e. there was a relevant prior encounter at a different hospital within the past year. ED encounters were at highest risk for missing clinical data (44.8% [39.8 – 50.4] of ED encounters vs. 34.5% [31.3 – 37.8] inpatient and 24.9% [22.6 – 27.4] outpatient). However, the largest number of encounters at risk for missing clinical data occurred in the outpatient setting (14,579 outpatient encounters vs. 5041 ED encounters and 1982 inpatient encounters). A comparatively smaller number and percentage of brain imaging studies were at risk for missing data: 381 encounters for any brain image (14.5% [11.5 – 18]), 256 encounters for head CTs (14.6% [11.1 – 18.4]), and 27 for brain MRI (3.9% [1.6 – 6.9]). (Table 1) DISCUSSION Summary We found that each of seven Manhattan hospitals provided care for a population of people with epilepsy who had also sought care at each of the other hospitals, in the outpatient, inpatient, ED, and radiology settings, as well as for brain imaging studies and head CTs. These seven hospitals do not have interoperable EHRs. Therefore the crossover patterns likely caused the treating clinicians to lack recently obtained relevant clinical data at nearly half of ED visits, a third of inpatient visits, a quarter of outpatient visits, and a quarter of radiology visits. Although ED visits had the highest percentage of encounters at risk missing clinical data, the number of outpatient visits at risk was three times larger. Innovation Our study is innovative for several reasons. First, we show that HIE can be used as a data source for the study of care patterns for people with epilepsy, in alignment with the Institute of Medicine’s recommendation to “continue and expand collaborative surveillance and data collection efforts” to improve care for people with epilepsy.1 Second, we expand the use of graph theoretic techniques39, 44 in the field of clinical informatics to explore differences in care patterns across different clinical settings. Third, we refine the methods to quantify information gaps generated when

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patients seek care from physicians who do not share health record systems. Our measurement of “encounters at risk for missing clinical data” only counts visits in which a patient is known to have been elsewhere previously, within a specified amount of time, in a relevant context. This approach improves upon the measurements reported in largedatabase studies34, 39 as it more closely approximates the way missing clinical data is quantified in chart review and survey studies.33, 37, 41 Our measurement is not specific to epilepsy, and could be readily used to estimate the potential value of HIE in other populations. Significance Several implications of these findings deserve mention. First, the extent of patient crossover and the magnitude of encounters at risk for missing clinical data among people with epilepsy indicate this population has substantial gaps in informational care continuity. They are therefore likely to benefit from HIE. HIE could help meet the Institute of Medicine’s recent recommendations to improve care for people with epilepsy1 by insuring that treating physicians have access to important clinical data. Second, the finding that 1 in 7 head CTs (256 of 1757) were performed without access to a prior head CT raises the possibility that some were unnecessarily duplicated.50 Although a head CT may be clinically warranted to evaluate a first seizure,51, 52 a brain MRI is the more definitive study,53 and head CTs should not be routinely used for people with established epilepsy. Overuse of CT scans is a significant public health concern, as it may increase the population risk for cancer due to ionizing radiation.54 Third, the network properties of the crossover patterns have implications for HIE architecture. The network graphs for inpatient, outpatient, ED, and radiology studies were all 100% complete – each is a “clique”, in the language of graph theory. Thus in order to provide fully informed care for the population of people with epilepsy at one hospital, the physicians would need access to the EHRs of every other hospitals in the group. A query-based architecture of HIE, supporting seven connections, would more efficiently manage data flow across the network, compared with an individual peer-to-peer exchange architecture (i.e. the Direct Project), which may require 21 functioning connections.32, 39 However, the clinical and financial value of the theoretical gain in efficiency remains unclear, especially given the increased costs of a query-based system. Well-supported peer-to-peer exchange between pairs of hospitals with high crossover would fill some information gaps, especially if the cost of a query-based system was prohibitive. Fourth, the absolute number of encounters at risk for missing clinical data in the outpatient setting was three times the number in the ED setting. Several recent evaluations of the value of HIE have concentrated on the ED setting55, 56 – our findings suggest that the magnitude of need in the outpatient setting is greater. HIE has been shown to improve outpatient office efficiency, and use of HIE is associated with improved ambulatory quality of care; however, further research is needed to establish clinical and financial value.57, 58   Limitations Our study has several important limitations. First, the crossover and missing data rates are likely underestimated for several reasons. Internal to NYCLIX, the linkage of patient records from different institutions may be impeded by inaccurate, duplicated, or comingled records within each institution,59 as well as by conservative linkage algorithms used to minimize false positive matches and avoid consequent medical errors. External to NYCLIX, we could not include encounters at sites not participating in NYCLIX, including at least 12 other Manhattan hospitals. Second, the generalizability of our findings may be limited given the unique geographic location of these hospitals. Manhattan is the most densely populated county in the US, suggesting similar network graphs drawn in other locations might be less densely connected. For example, a study of 96 Indiana EDs found the connectedness to be high (85%) but not fully connected.39

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Third, we were unable to validate the ICD-9 based epilepsy definition in this population. The NYCLIX research policy only allows creation of de-identified data sets for research. ICD-9 based definitions of epilepsy tend to be more specific than sensitive,43 suggesting we may have under-ascertained people with epilepsy in this data set. Conclusion & Next Steps Clinicians treating people with epilepsy frequently do not have access to recent, relevant clinical data. Thus HIE is likely to provide significant value for people with epilepsy. Future efforts to develop HIE for this population should expand their focus to the outpatient setting, where the largest number of patient encounters might benefit from access to electronic health information from unaffiliated institutions. Epilepsy is only one of a group of ambulatory care sensitive conditions, which also includes diseases like asthma, congestive heart failure, and diabetes.13-16 Further investigation of crossover and missing clinical data for people with these diseases may give important insights into the role of HIE to improve their care. ACKNOWLEDGEMENTS We thank the other faculty members at the Center for Healthcare Informatics and Policy for their review and feedback of several iterations of this work, Laurence Berg at Healthix Inc and Arit Onyle at the Mount Sinai School of Medicine for their invaluable assistance obtaining and interpreting the data from NYCLIX, Dr. Christine Bower for drawing attention to the fact that epilepsy is an ambulatory sensitive condition, and Dr. Barry Kosofsky for his leadership and his ongoing support. FUNDING This project was supported in part by the National Institute for Neurologic Disease and Stroke grant #K12-NS0662 (ZG). This project was also supported in part by funds from the Clinical Translational Science Center (CTSC), National Center for Advancing Translational Sciences (NCATS) grant #UL1-RR024996 (SB). The content is solely the responsibility of the authors and does not necessarily represent the official views of the CTSC funding source NCATS based in Rockville, MD. REFERENCES

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Potential value of health information exchange for people with epilepsy: crossover patterns and missing clinical data.

For people with epilepsy, the potential value of health information exchange (HIE) is unknown...
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