Jason S Shapiro

Icahn School of Medicine at Mount Sinai, Borough of Manhattan, New York, United States

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Publications (54)127.65 Total impact

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    ABSTRACT: Researchers have attempted to optimize imaging utilization by describing which clinical variables are more predictive of acute disease and, conversely, what combination of variables can obviate the need for imaging. These results are then used to develop evidence-based clinical pathways, clinical decision instruments, and clinical practice guidelines. Despite the validation of these results in subsequent studies, with some demonstrating improved outcomes, their actual use is often limited. This article outlines a research agenda to promote the dissemination and implementation (also known as knowledge translation) of evidence-based interventions for emergency department (ED) imaging, i.e., clinical pathways, clinical decision instruments, and clinical practice guidelines. We convened a multidisciplinary group of stakeholders and held online and telephone discussions over a 6-month period culminating in an in-person meeting at the 2015 Academic Emergency Medicine consensus conference. We identified the following four overarching research questions: 1) what determinants (barriers and facilitators) influence emergency physicians' use of evidence-based interventions when ordering imaging in the ED; 2) what implementation strategies at the institutional level can improve the use of evidence-based interventions for ED imaging; 3) what interventions at the health care policy level can facilitate the adoption of evidence-based interventions for ED imaging; and 4) how can health information technology, including electronic health records, clinical decision support, and health information exchanges, be used to increase awareness, use, and adherence to evidence-based interventions for ED imaging? Advancing research that addresses these questions will provide valuable information as to how we can use evidence-based interventions to optimize imaging utilization and ultimately improve patient care.
    Academic Emergency Medicine 11/2015; DOI:10.1111/acem.12830 · 2.01 Impact Factor
  • E. Kim · T. Lowry · G.T. Loo · B.D. Shy · U. Hwang · N. Genes · L.D. Richardson · C.F. Clesca · J.S. Shapiro ·

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    ABSTRACT: To describe (1) the predictability of frequent emergency department (ED) use (a marker of inadequate disease control and/or poor access to care), and (2) the demographics, comorbidities, and use of health services of frequent ED users, among people with epilepsy. We obtained demographics, comorbidities, and 2 years of encounter data for 8,041 people with epilepsy from a health information exchange in New York City. Using a retrospective cohort design, we explored bivariate relationships between baseline characteristics (year 1) and subsequent frequent ED use (year 2). We then built, evaluated, and compared predictive models to identify frequent ED users (≥4 visits year 2), using multiple techniques (logistic regression, lasso, elastic net, CART [classification and regression trees], Random Forests, AdaBoost, support vector machines). We selected a final model based on performance and simplicity. People with epilepsy who, in year 1, were adults (rather than children or seniors), male, Manhattan residents, frequent users of health services, users of multiple health systems, or had medical, neurologic, or psychiatric comorbidities, were more likely to frequently use the ED in year 2. Predictive techniques identified frequent ED visitors with good positive predictive value (approximately 70%) but poor sensitivity (approximately 20%). A simple strategy, selecting individuals with 11+ ED visits in year 1, performed as well as more sophisticated models. People with epilepsy with 11+ ED visits in a year are at highest risk of continued frequent ED use and may benefit from targeted intervention to avoid preventable ED visits. Future work should focus on improving the sensitivity of predictions. © 2015 American Academy of Neurology.
    Neurology 08/2015; DOI:10.1212/WNL.0000000000001944 · 8.29 Impact Factor
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    ABSTRACT: Emergency physicians often must make critical, time-sensitive decisions with a paucity of information with the realization that additional unavailable health information may exist. Health information exchange enables clinician access to patient health information from multiple sources across the spectrum of care. This can provide a more complete longitudinal record, which more accurately reflects the way most patients obtain care: across multiple providers and provider organizations. This information article explores various aspects of health information exchange that are relevant to emergency medicine and offers guidance to emergency physicians and to organized medicine for the use and promotion of this emerging technology. This article makes 5 primary emergency medicine-focused recommendations, as well as 7 additional secondary generalized recommendations, to health information exchanges, policymakers, and professional groups, which are crafted to facilitate health information exchange's purpose and demonstrate its value. Copyright © 2015 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.
    Annals of emergency medicine 07/2015; DOI:10.1016/j.annemergmed.2015.06.018 · 4.68 Impact Factor
  • N Garg · G Husk · T Nguyen · A Onyile · S Echezona · G Kuperman · J S Shapiro ·
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    ABSTRACT: Hospital closures are becoming increasingly common in the United States. Patients who received care at the closing hospitals must travel to different, often farther hospitals for care, and nearby remaining hospitals may have difficulty coping with a sudden influx of patients. Our objectives are to analyze the dispersion patterns of patients from a closing hospital and to correlate that with distance from the closing hospital for three specific visit types: emergency, inpatient, and ambulatory. In this study, we used data from a health information exchange to track patients from Saint Vincent's Medical Center, a hospital in New York City that closed in 2010, to determine where they received emergency, inpatient, and ambulatory care following the closure. We found that patients went to the next nearest hospital for their emergency and inpatient care, but ambulatory encounters did not correlate with distance. It is likely that patients followed their ambulatory providers as they transitioned to another hospital system. Additional work should be done to determine predictors of impact on nearby hospitals when another hospital in the community closes in order to better prepare for patient dispersion.
    Applied Clinical Informatics 04/2015; 6(1):185-99. DOI:10.4338/ACI-2014-10-RA-0090 · 0.39 Impact Factor
  • John Zech · Gregg Husk · Thomas Moore · Gilad J Kuperman · Jason S Shapiro ·
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    ABSTRACT: 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. 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. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
    Journal of the American Medical Informatics Association 02/2015; 22(3). DOI:10.1093/jamia/ocu005 · 3.50 Impact Factor
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    ABSTRACT: Objectives“Hospital crossover” occurs when people visit multiple hospitals for care, which may cause gaps in electronic health records. Although crossover is common among people with epilepsy, the effect on subsequent use of health services is unknown. Understanding this effect will help prioritize health care delivery innovations targeted for this population.Methods We collected de-identified information from a health information exchange network describing 7,836 people with epilepsy who visited any of seven hospitals in New York, NY from 2009–2012. Data included demographics, comorbidities, and 2 years of visit information from ambulatory, inpatient, emergency department (ED), and radiology settings. We performed two complementary retrospective cohort analyses, in order to (1) illustrate the effect on a carefully selected subgroup, and (2) confirm the effect across the study population. First, we performed a matched cohort analysis on 410 pairs of individuals with and without hospital crossover in the baseline year. Second, we performed a propensity score odds weighted ordinal logistic regression analysis to estimate the effect across all 7,836 individuals. The outcomes were the use of six health services in the follow-up year.ResultsIn the matched pair analysis, baseline hospital crossover increased the odds of more visits in the ED (odds ratio 1.42, 95% confidence interval [CI] 1.05–1.95) and radiology settings (1.7, 1.22–2.38). The regression analysis confirmed the ED and radiology findings, and also suggested that crossover led to more inpatient admissions (1.35, 1.11–1.63), head CTs (1.44, 1.04–2), and brain MRIs (2.32, 1.59–3.37).SignificanceBaseline hospital crossover is an independent marker for subsequent increased health service use in multiple settings among people with epilepsy. Health care delivery innovations targeted for people with epilepsy who engage in hospital crossover should prioritize (1) sharing radiology images and reports (to reduce unnecessary radiology use, particularly head CTs), and (2) improving coordination of care (to reduce unnecessary ED and inpatient use).
    Epilepsia 01/2015; 56(1). DOI:10.1111/epi.12882 · 4.57 Impact Factor
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    William Fleischman · Tina Lowry · Jason Shapiro ·
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    ABSTRACT: 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.
    12/2014; 2(1):Article 19. DOI:10.13063/2327-9214.1099

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    ABSTRACT: In a health care system where patients often have numerous providers and multiple chronic medical conditions, interoperability of health information technology (HIT) is of paramount importance. Regional health information organizations (RHIO) often provide a health information exchange (HIE) as a solution, which gives stakeholders access to clinical data that they otherwise would not otherwise have. A secondary use of preexisting HIE infrastructure is clinical event notification (CEN) services, which send automated notifications to stakeholders. This paper describes the development and implementation of a CEN service enabled by a RHIO in the New York metropolitan area to improve care coordination for patients enrolled in a geriatric emergency department care coordination program. This operational CEN system incorporates several innovations that to our knowledge have not been implemented previously. They include the near real-time notifications and the delivery of notifications via multiple pathways: electronic health record (EHR) "in-baskets," email, text message to internet protocol-based "zone" phones, and automated encounter entry into the EHR. Based on these alerts the geriatric care coordination team contacts the facility where the patient is being seen and offers additional information or assistance with disposition planning with the goal of decreasing potentially avoidable admissions and duplicate testing. During the nearly one-year study period, the CEN program enrolled 5722 patients and sent 497 unique notifications regarding 206 patients. Of these notifications, 219 (44%) were for emergency department (ED) visits; 121 (55%) of those notifications were received during normal business hours when the care coordination team was available to contact the ED where the patient was receiving care. Hospital admissions resulted from 45% of ED visits 17.8% of these admissions lasted 48 hours or less, suggesting some might potentially be avoidable. This study demonstrates the potential of CEN systems to improve care coordination by notifying providers of the occurrence of specific events. Although it could not directly be demonstrated here, we believe that widespread use of CEN systems have potential to reduce potentially avoidable admissions and duplicate testing, likely leading to decreased costs.
    08/2014; 2(3):1095. DOI:10.13063/2327-9214.1095
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    ABSTRACT: For over 25 years, emergency medicine researchers have examined 72-hour return visits as a marker for high-risk patient visits and as a surrogate measure for quality of care. Individual emergency departments (EDs) frequently use 72-hour returns as a screening tool to identify deficits in care, although comprehensive departmental reviews of this nature may consume considerable resources. We discuss the lack of published data supporting the use of 72-hour return frequency as an overall performance measure and examine why this is not a valid use, describe a conceptual framework for reviewing 72-hour return cases as a screening tool, and call for future studies to test various models for conducting such QA reviews of patients who return to the ED within 72 hours.
    American Journal of Emergency Medicine 08/2014; 33(1). DOI:10.1016/j.ajem.2014.08.005 · 1.27 Impact Factor
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    ABSTRACT: Objective Hospital crossover occurs when people seek care at multiple hospitals, creating information gaps for physicians at the time of care. Health information exchange (HIE) is technology that fills these gaps, by allowing otherwise unaffiliated physicians to share electronic medical information. However, the potential value of HIE is understudied, particularly for chronic neurologic conditions like epilepsy. We describe the prevalence and associated factors of hospital crossover among people with epilepsy, in order to understand the epidemiology of who may benefit from HIE. Methods We used a cross-sectional study design to examine the bivariate and multivariable association of demographics, comorbidity, and health service utilization variables with hospital crossover, among people with epilepsy. We identified 8,074 people with epilepsy from the International Classification of Diseases, Ninth Revision (ICD-9) codes, obtained from an HIE that linked seven hospitals in Manhattan, New York. We defined hospital crossover as care from more than one hospital in any setting (inpatient, outpatient, emergency, or radiology) over 2 years. ResultsOf 8,074 people with epilepsy, 1,770 (22%) engaged in hospital crossover over 2 years. Crossover was associated with younger age (children compared with adults, adjusted odds ratio [OR] 1.4, 95% confidence interval [CI] 1.2–1.7), living near the hospitals (Manhattan vs. other boroughs of New York City, adjusted OR 1.6, 95% CI 1.4–1.8), more visits in the emergency, radiology, inpatient, and outpatient settings (p < 0.001 for each), and more head computerized tomography (CT) scans (p < 0.01). The diagnosis of “encephalopathy” was consistently associated with crossover in bivariate and multivariable analyses (adjusted OR 2.66, 95% CI 2.14–3.29), whereas the relationship between other comorbidities and crossover was less clear. SignificanceHospital crossover is common among people with epilepsy, particularly among children, frequent users of medical services, and people living near the study hospitals. HIE should focus on these populations. Further research should investigate why hospital crossover occurs, how it affects care, and how HIE can most effectively mitigate the resultant fragmentation of medical records.A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.
    Epilepsia 02/2014; 55(5). DOI:10.1111/epi.12552 · 4.57 Impact Factor
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    ABSTRACT: Health information exchange (HIE) provides an essential enhancement to electronic health records (EHR), allowing information to follow patients across provider organizations. There is also an opportunity to improve public health surveillance, quality measurement, and research through secondary use of HIE data, but data quality presents potential barriers. Our objective was to validate the secondary use of HIE data for two emergency department (ED) quality measures: identification of frequent ED users and early (72-hour) ED returns. We compared concordance of various demographic and encounter data from an HIE for four hospitals to data provided by the hospitals from their EHRs over a two year period, and then compared measurement of our two quality measures using both HIE and EHR data. We found that, following data cleaning, there was no significant difference in the total counts for frequent ED users or early ED returns for any of the four hospitals (p<0.001).
    AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 01/2014; 2014:573-9.
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    ABSTRACT: 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 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.
    Health Affairs 12/2013; 32(12):2193-8. DOI:10.1377/hlthaff.2013.0167 · 4.97 Impact Factor
  • J.S. Shapiro · A. Onyile · N. Genes · C. DiMaggio · G. Kuperman · L.D. Richardson ·

    Annals of Emergency Medicine 10/2013; 62(4):S94. DOI:10.1016/j.annemergmed.2013.07.082 · 4.68 Impact Factor
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    ABSTRACT: The Health Information Technology for Economic and Clinical Health Act of 2009 and the Centers for Medicare & Medicaid Services "meaningful use" incentive programs, in tandem with the boundless additional requirements for detailed reporting of quality metrics, have galvanized hospital efforts to implement hospital-based electronic health records. As such, emergency department information systems (EDISs) are an important and unique component of most hospitals' electronic health records. System functionality varies greatly and affects physician decisionmaking, clinician workflow, communication, and, ultimately, the overall quality of care and patient safety. This article is a joint effort by members of the Quality Improvement and Patient Safety Section and the Informatics Section of the American College of Emergency Physicians. The aim of this effort is to examine the benefits and potential threats to quality and patient safety that could result from the choice of a particular EDIS, its implementation and optimization, and the hospital's or physician group's approach to continuous improvement of the EDIS. Specifically, we explored the following areas of potential EDIS safety concerns: communication failure, wrong order-wrong patient errors, poor data display, and alert fatigue. Case studies are presented that illustrate the potential harm that could befall patients from an inferior EDIS product or suboptimal execution of such a product in the clinical environment. The authors have developed 7 recommendations to improve patient safety with respect to the deployment of EDISs. These include ensuring that emergency providers actively participate in selection of the EDIS product, in the design of processes related to EDIS implementation and optimization, and in the monitoring of the system's ongoing success or failure. Our recommendations apply to emergency departments using any type of EDIS: custom-developed systems, best-of-breed vendor systems, or enterprise systems.
    Annals of emergency medicine 06/2013; 62(4). DOI:10.1016/j.annemergmed.2013.05.019 · 4.68 Impact Factor
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    ABSTRACT: Objective: To quantify the percentage of records with matching identifiers as an indicator for duplicate or potentially duplicate patient records in electronic health records in five different healthcare organisations, describe the patient safety issues that may arise, and present solutions for managing duplicate records or records with matching identifiers. Methods: For each institution, we retrieved deidentified counts of records with an exact match of patient first and last names and dates of birth and determined the number of patient records existing for the top 250 most frequently occurring first and last name pairs. We also identified methods for managing duplicate records or records with matching identifiers, reporting the adoption rate of each across institutions. Results: The occurrence of matching first and last name in two or more individuals ranged from 16.49% to 40.66% of records; inclusion of date of birth reduced the rates to range from 0.16% to 15.47%. The number of records existing for the most frequently occurring name at each site ranged from 41 to 2552. Institutions varied widely in the methods they implemented for preventing, detecting and removing duplicate records, and mitigating resulting errors. Conclusions: The percentage of records having matching patient identifiers is high in several organisations, indicating that the rate of duplicate records or records may also be high. Further efforts are necessary to improve management of duplicate records or records with matching identifiers and minimise the risk for patient harm.
    BMJ quality & safety 01/2013; 22(3). DOI:10.1136/bmjqs-2012-001419 · 3.99 Impact Factor
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    ABSTRACT: For people with epilepsy, the potential value of health information exchange (HIE) is unknown. 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." 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). People with epilepsy may benefit from HIE. Future HIE initiatives should prioritize outpatient access.
    AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 01/2013; 2013:527-36.

Publication Stats

364 Citations
127.65 Total Impact Points


  • 2004-2015
    • Icahn School of Medicine at Mount Sinai
      • Department of Emergency Medicine
      Borough of Manhattan, New York, United States
  • 2013
    • Mount Sinai Medical Center
      • Department of Emergency Medicine
      New York, New York, United States
  • 2005-2011
    • Columbia University
      • Department of Biomedical Informatics
      New York, New York, United States
  • 2010
    • Georgia Health Sciences University
      • Medical College of Georgia
      Augusta, Georgia, United States