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. 2014 Dec 9;2(1):1099. doi: 10.13063/2327-9214.1099

The Visit-Data Warehouse: Enabling Novel Secondary Use of Health Information Exchange Data

William Fleischman i,ii,iii, Tina Lowry iv, Jason Shapiro i
PMCID: PMC4371519  PMID: 25848595

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.

Keywords: informatics, health information technology, quality, health information exchange

Introduction

Health Information Exchange (HIE) efforts have formed nationwide over the past 25 years to integrate a patchwork of medical information among health care systems and providers.1 A recent survey identified 315 such initiatives nationwide.2 These efforts inherently face significant challenges in implementation,3 usage and adoption,4,5 and the building of a sustainable business model.6,7 While there is some evidence that HIE can decrease costs by reducing potentially avoidable hospital admissions8 and duplicate diagnostic testing,9,10 definitive evidence of the quality and safety benefits of HIE is lacking.11 The recent wave of consolidation of health care provider organizations may in some cases render HIEs obsolete, since an entire region consolidated under one organization would be able to access data more easily, but complete regional control of health care by one organization is likely to remain rare given antitrust regulations.12

HIEs have generally formed for the primary clinical use of an individual provider searching for clinical information on an individual patient across multiple health care provider organizations where the patient has received care. Now many secondary uses have been proposed and are being piloted including HIE-based notification services to support care management,13 quality measurement and improvement,14 public health,15–18 and others.19 HIEs face challenges with data quality as it is,20,21 but this becomes especially problematic when data is leveraged for uses beyond the primary clinical use.22–25 In this paper we describe a secondary data infrastructure that was built on top of a functioning HIE platform in order to support secondary use and prevent potentially negative impacts these uses might have on HIE system performance by inundating servers and slowing down patient-care queries.

Description of the HIE and Base Infrastructure

Healthix is an HIE based in the New York metropolitan region. As of January, 2014, Healthix comprised 107 participating organizations with 29,946 acute care beds in 383 facilities, included more than 9.2 million unique patients, and had approximately 6,500 users performing more than 10,000 searches per month.

Healthix’s infrastructure (Figure 1) is based on the InterSystems vendor platform (Intersystems, Cambridge, Mass.), which is designed to support interoperability across multiple health care systems and is installed and in use by multiple heath information organizations in Healthix to support HIE functionality. Each participating health care organization has a dedicated Caché “edge” or local server. All edge servers utilize a common data model, each with their respective site’s patient-level encounter and clinical data. This network of edge servers allows them to act as a single distributed database. Caché is a proprietary hierarchical database system developed using the M or MUMPS programming language and is optimized for transactional use. It differs from relational databases such as SQL Server that are optimized for queries and reporting.26 This means that the HIE’s Caché platform is designed to support the primary use case of individual queries looking for detailed information on single patients, but is not well suited for cross-population queries that are often needed for secondary use.

Figure 1.

Figure 1.

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Workflow Diagram for the Interaction between the VDW and the Primary HIE Infrastructure

When patients register for an encounter at participating organizations, their demographic information is sent in real time across an interface to the hub’s master patient index (MPI) using standard Health Level 727 (HL7) messages and utilizing virtual private network (VPN) encryption technology to maintain security. The MPI performs a probabilistic match based on the patient demographic information,28 uses predetermined thresholds tuned to maintain a low false-positive match rate to link individual patients to records across multiple sites, and assigns a unique enterprise patient identifier (MPI ID). Clinicians generally look up patient information using a Web-based portal, which queries the MPI for patient records at the various sites. If records are found, a “record locator service” at the hub creates pointers to the patient’s clinical data on the various edge servers, allowing the data to be viewed in the Web-based portal. This federated technical platform,29 which supports the primary clinical use case, has been operational since 2008 and allows each site to maintain physical stewardship of their own clinical data since only the patient demographic data is centralized at the HIE’s hub.

Issues with the Base Infrastructure

The HIE architecture was designed to support the primary clinical use of patient record lookups through a clinical portal as described above, and this results in hundreds or thousands of transactions per minute. There was concern that layering additional services for secondary use cases on top of it would cause server performance issues, since additional demands on the existing technical platform could cause slow response times for end users making clinical portal queries. Additionally, because the operational platform utilizes the proprietary Caché hierarchical data structure, we needed to have a more common relational database model in place for developing custom software and performing analytics. As requests for secondary uses of HIE data became more prominent, the need for a separate, real-time database to support them became necessary and led to the development of the visit-data warehouse (VDW).

Secondary-Use Cases That Prompted the Development of the Visit-Data Warehouse

Four specific proposed uses prompted the creation of the VDW. First, as part of a Centers for Medicare & Medicaid Services (CMS) funded project to improve geriatric emergency care in a new dedicated geriatric emergency department (ED), it was proposed initially to use HIE data to track metrics through a weekly report including the following: (1) average number of ED visits per patient per year, (2) average number of hospitalizations per patient per year, (3) average number of 30-day hospital readmissions following hospital discharge per patient per year, (4) average number of 6-month hospital readmissions following hospital discharge per patient per year, and (5) average number of 72-hour ED return visits following a treat and release visit from the ED per patient per year.

Second, as part of the same CMS-funded project, and as an enhancement to the weekly reports, the creation of a dashboard was also planned, using standard business intelligence tools. The dashboard provides a visual display as well as a tabular output of near-real-time, de-identified encounter data from the VDW. Built-in queries are based on the above five metrics with additional parameters based on user input for date range, hospital name, report type, and patient age range. The results are exportable in standard format for additional analysis. The user is able to view information on a summary level (all hospitals with aggregated results) and at a detail level (specific hospital to patient level information).

Third, frequent ED use has been identified as a general marker of high utilization of health care resources,30 and frequent ED users as a population may potentially benefit from intervention such as intensive case management.31 Use of HIE data has been previously shown to significantly increase the ability to identify frequent ED users.14 While previous efforts have identified potential candidates for intervention via retrospective analysis of ED visits,32,33 the VDW will be used to detect, in near real time, when any patient registers for a fourth ED visit within 30 days across all EDs in the HIE. Such detection then triggers an automatic notification to an ED case manager to screen the patient for potential benefit of additional services aimed at reducing ED recidivism. This feature has been developed and is undergoing technical testing against the VDW data source.

Fourth, patients who revisit the ED within 72 hours of ED discharge may represent errors in diagnosis and management.34,35 Furthermore, a patient receiving suboptimal care at the first visit might be more likely to visit another hospital for that “return” visit. Indeed, a preliminary study across a relatively small HIE showed increased detection of patients visiting an ED again within 72 hours by 11.6 percent when compared to individual, site-specific ED analysis.36 The analysis included only a small portion of EDs in that region, possibly underestimating the effect 72-hour returns analysis would show if a substantial portion of the region’s EDs were included. The VDW allows a monthly report to be used as the basis of improving an ongoing 72-hour returns, continuous quality-improvement program by providing information on return visits to 31 hospitals participating in Healthix. The latter two secondary-use case projects were funded by a grant from the Agency for Healthcare Research & Quality (AHRQ).

Visit-Data Warehouse Infrastructure

The VDW (Figure 1) is built on a Microsoft SQL Server database model using specific HL7 messages and utilizes an “events listener” database, which captures a predetermined list of patient events based on those HL7 messages. Specific data elements in each message include hospital name, patient medical record number, admission- and discharge dates and times, encounter type (emergency, inpatient or outpatient), and a unique identifier for the current visit. Once an event is captured, a patient lookup is performed using the MPI service. The VDW database is then linked to a view of the MPI table. Using the MPI ID, the patient can be tracked across multiple sites within a specific time frame. Essentially, the VDW provides a separate, near-real-time relational database model for all inpatient and ED encounters across the entire HIE that is updated on an hourly basis, which can then be utilized for secondary use cases and for analytics and reporting using standard business intelligence tools.

Many electronic health records (EHRs) use Caché-like hierarchical databases for routine transactions, while moving data to a separate data warehouse based on a relational database, such as SQL server, on a regular basis (usually nightly) to be available for queries. The VDW is based on this model but its structure allows near-real-time updates of the data to support alerts for use cases that are time sensitive, something that EHR-based data warehouses do not typically support. Additionally, the VDW differs from EHR-based data warehouses in that it is built on an HIE and offers analytics and reporting capabilities across 31 hospitals, many of which have their own separate EHRs and data warehouses that are not able to interoperate directly with one another. This allows the VDW to harmonize data across multiple care settings in order to facilitate reporting and analytics across the entire HIE.

Development of the VDW is complete, and it is deployed in a “stage” environment to undergo technical and data validation testing. As an example, during a 24-hour period on July 25, 2014, there were 21,724 ED visit “admission, discharge, or transfer” (ADT) transactions; the average latency between the ADT message being sent from the local server and the update taking place on the VDW’s events listener database was 2.56 minutes (SD 10.5). We set the VDW to update on an hourly basis since this was all that was required by the use cases discussed above.

Early Lessons Learned from Visit-Data Warehouse (VDW) Development and Testing

Initial implementation and testing of the VDW in 2014 and feedback from testing protocols used to validate the VDW data led to some important lessons learned, which might apply to others embarking on similar projects. The following paragraphs describe these lessons learned.

Nonstandard Implementation of HL7 2.X

Although HL7 provides standard message types for the transactions of interest—namely admissions (using an HL7 “ADT^A01” message), discharges (using an HL7 “ADT^A02” message), and updates (using an HL7 “ADT^A08” message)—HL7 has not been consistently implemented by all 31 hospitals contributing data to the VDW. For instance, some hospitals are sending repeated HL7 updates as A01 messages, which are typically used for admission messages, not for updates. This has made it very difficult to accurately define the beginning and end of encounters across sites, and will likely require the creation of a mapping table to translate the flow of various message types from across multiple providers to a common standard, since the various message types do not necessarily have the same meaning from one site to the next.

Missing HL7 Messages

Since the HIE was originally built, beginning in 2008, there have been variations in the HL7 messages sent from various sites, with some of the sites only sending A01 admission messages for ED visits without a corresponding A03 discharge message to end the visit. Since the discharge date and time is never displayed in the clinical portal, and as this was not an absolute requirement for the primary clinical use case, the implementation decision was made to place the date and time from the A01 admission message in both the ED admit-date and time and the ED discharge-date and time fields so that the two values are equal. This was necessary in order to close out the visit since the HIE platform required an entry for discharge date and time. While this does not affect most of the above-described intended uses of the VDW, it does pose problems when calculating 72-hour ED returns. Seventy-two hour returns are typically calculated from the discharge time of the first visit to the admission time of the second visit, and since ED visits sometimes extend over days, the margin of error may approach the 72-hour mark in some instances. To correct this, imputation techniques based on average ED length of stay may be used during our analyses, but a permanent fix will need to be implemented at the Healthix interface level on a site by site basis.

Data Latency and Automated Monitoring

The VDW was designed with a near-real-time data “currency” goal, aiming to have all data current within one hour. Because there are a large number of HL7 messages flowing from the HIE’s Caché database edge servers to the events listener database and then to the VDW, with an MPI lookup being performed in order to link patients across sites, there are multiple potential points of failure that could prevent information from being updated in a timely manner. As part of testing, a daily report is run that looks for problems caused by interfaces malfunctioning or by messages queuing up. The report calculates current time minus the last message time for each site, then color codes the results as green (< 1 hour), yellow (2–12 hours), or red (> 12 hours). The report also tracks system downtimes by site, showing periods of missing data over the course of the last month so data can be restored through a separate process. As a more permanent solution, automated monitoring tools are planned for at various critical points in the information flow described in Figure 1 that will automatically notify systems administrators if data stops flowing at an expected rate across various junction points.

Governance Issues

The Healthix Participant Agreement, signed by all participating organizations, and the Healthix Privacy and Security Policy, approved by the Healthix Board of Directors, both provide for the sharing of clinical and encounter data across the HIE for clinical care coordination and quality assurance purposes, provided that the patient has given consent. Since the secondary use cases described above all fall within the Healthix definition of care coordination or quality assurance, the projects themselves posed no issue from a governance standpoint. However, since Healthix was set up as a federated HIE in order to allow each organization to maintain stewardship of its own clinical data through the use of dedicated edge servers at each site, setting up this centralized encounter-data warehouse was met with some concerns. The trust and close communication built among the participating organizations over the years through the Healthix governance structure, which includes a Board of Directors and Privacy and Security, Clinical Advisory, and Data Use committees, was an important factor in overcoming initial reluctance and allowing the VDW creation.

Suggestions for Future Use and Implementations

In addition to the four secondary uses described earlier, other potential use cases include 30-day readmission alerts to empower various care-coordination efforts aimed at decreasing these events. In this use case, care managers would be alerted that a recently discharged patient has registered in an ED or been admitted at any of the HIE sites. Another potential use case is a duplicate imaging alerting system. This would allow a real-time, clinical-decision support alert to be displayed at the point-of-order entry if a patient for whom a clinician is ordering an imaging test, such as a CT scan, has had multiple prior similar tests. This functionality would require the addition of indicator variables for imaging studies to the VDW. We are exploring both of these potential uses for implementation into our HIE.

Although we believe this work should be easily generalizable, further work should be done to assess the various needs of other HIE efforts across the country to enable secondary use, and to assess how requirements for parallel data infrastructures might vary. HIEs that use a centralized data model with a single database rather than the federated model our HIE uses would likely also face the issue of server overload in a real-time reporting system requiring the separate reporting database we describe. While the implementation described in this paper was “homegrown,” there are various vendor platforms that could potentially be used to accomplish the same. We would have likely faced similar implementation challenges with other platforms since the issues we describe revolve around data sources and governance.

Besides the technical issues described above, much work needs to be done to develop standard ways of measuring data quality in HIE environments, and to validate the use of HIE data for secondary uses.21,23,24 Once adequate data has accumulated in the VDW it should serve as a good platform for performing descriptive analyses to ensure that the data is of sufficient quality by further testing for data concordance, comprehensiveness, and timeliness.

As mentioned, HIEs face challenges in building sustainable business models. We believe HIEs can use data from their existing primary infrastructure, as we did, to showcase the potential benefits of secondary uses when attempting to procure funding for building this secondary use infrastructure.

Conclusions

In summary, secondary use of HIE data holds significant potential benefit in areas such as quality measurement, population management, and care coordination. If leveraged appropriately, these secondary uses could prove even more valuable than the primary clinical use case for which most HIE networks were developed. In this paper we describe the creation of a parallel infrastructure that takes advantage of existing HIE systems to enable novel secondary uses of the data despite the distributed and proprietary nature of the existing HIE platform. This new infrastructure allowed development of secondary use cases without compromising the performance of the existing primary HIE platform. HIE data should allow for more accurate measurement of various metrics, reflecting the way in which many patients access health care across multiple providers. However, adequate infrastructure must be developed to support secondary data use in an HIE environment, and steps must be taken to ensure HIE data quality.

Acknowledgments

The projects described in this paper were supported by grant number 1C1CMS331055 from the Department of Health and Human Services, Centers for Medicare & Medicaid Services, and by grant number R01HS021261 from the Agency for Healthcare Research and Quality. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Department of Health and Human Services or any of its agencies. We thank Mukul Govil, BS, from the New York eHealth Collaborative (NYeC) for his work on the visit data store.

References

  • 1.Vest JR, Gamm LD. Health information exchange: persistent challenges and new strategies. J Am Med Inform Assoc. 2010;17(3):288–94. doi: 10.1136/jamia.2010.003673. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.eHealth Initiative. Results From Survey on Health Data Exchange 2013 [Internet] Washington, D.C.: 2013. Available from: http://www.ehidc.org/resource-center/publications/view_document/333-survey-results-results-from-survey-on-data-exchange-2013-data-exchange. [Google Scholar]
  • 3.for the HITEC Investigators. Vest JR, Campion TR, Kaushal R. Challenges, Alternatives, and Paths to Sustainability for Health Information Exchange Efforts. J Med Syst [Internet] 2013 Dec;37(6) doi: 10.1007/s10916-013-9987-7. [cited 2014 Apr 3]; Available from: http://link.springer.com/10.1007/s10916-013-9987-7. [DOI] [PubMed] [Google Scholar]
  • 4.Genes N, Shapiro J, Vaidya S, Kuperman G. Adoption of Health Information Exchange by Emergency Physicians at Three Urban Academic Medical Centers. Appl Clin Inform. 2011 Jul 13;2(3):263–9. doi: 10.4338/ACI-2011-02-CR-0010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Rudin R, Volk L, Simon S, Bates D. What Affects Clinicians’ Usage of Health Information Exchange? Appl Clin Inform. 2011 Jul 6;2(3):250–62. doi: 10.4338/ACI-2011-03-RA-0021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Adler-Milstein J, Bates DW, Jha AK. Operational health information exchanges show substantial growth, but long-term funding remains a concern. Health Aff (Millwood) 2013;32(8):1486–92. doi: 10.1377/hlthaff.2013.0124. [DOI] [PubMed] [Google Scholar]
  • 7.Frohlich J, Karp S, Smith MD, Sujansky W. Retrospective: lessons learned from the Santa Barbara project and their implications for health information exchange. Health Aff (Millwood) 2007;26(5):w589–w591. doi: 10.1377/hlthaff.26.5.w589. [DOI] [PubMed] [Google Scholar]
  • 8.Ben-Assuli O, Shabtai I, Leshno M. The impact of EHR and HIE on reducing avoidable admissions: controlling main differential diagnoses. BMC Med Inform Decis Mak. 2013;13(1):49. doi: 10.1186/1472-6947-13-49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Frisse ME, Johnson KB, Nian H, Davison CL, Gadd CS, Unertl KM, et al. The financial impact of health information exchange on emergency department care. J Am Med Inform Assoc. 2012 May 1;19(3):328–33. doi: 10.1136/amiajnl-2011-000394. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Overhage JM, Dexter PR, Perkins SM, Cordell WH, McGoff J, McGrath R, et al. A randomized, controlled trial of clinical information shared from another institution. Ann Emerg Med. 2002 Jan;39(1):14–23. doi: 10.1067/mem.2002.120794. [DOI] [PubMed] [Google Scholar]
  • 11.Kaelber DC, Bates DW. Health information exchange and patient safety. J Biomed Inform. 2007 Dec;40(6):S40–S45. doi: 10.1016/j.jbi.2007.08.011. [DOI] [PubMed] [Google Scholar]
  • 12.Dafny L. Hospital Industry Consolidation—Still More to Come? N Engl J Med. 2014;370(3):198–9. doi: 10.1056/NEJMp1313948. [DOI] [PubMed] [Google Scholar]
  • 13.Moore T, Shapiro JS, Doles L, Calman N, Camhi E, Check T, et al. Event Detection: a clinical notification service on a health information exchange platform. AMIA Annual Symposium Proceedings [Internet]; American Medical Informatics Association; 2012. p. 635. [cited 2014 Apr 7]. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540505/ [PMC free article] [PubMed] [Google Scholar]
  • 14.Shapiro JS, Johnson SA, Angiollilo J, Fleischman W, Onyile A, Kuperman G. Health Information Exchange Improves Identification Of Frequent Emergency Department Users. Health Aff (Millwood) 2013 Dec 3;32(12):2193–8. doi: 10.1377/hlthaff.2013.0167. [DOI] [PubMed] [Google Scholar]
  • 15.Shapiro JS, Mostashari F, Hripcsak G, Soulakis N, Kuperman G. Using health information exchange to improve public health. Am J Public Health. 2011;101(4) doi: 10.2105/AJPH.2008.158980. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Merrill JA, Deegan M, Wilson RV, Kaushal R, Fredericks K. A system dynamics evaluation model: implementation of health information exchange for public health reporting. J Am Med Inform Assoc. 2013 Jun 1;20(e1):e131–e138. doi: 10.1136/amiajnl-2012-001289. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Mandl KD. Implementing Syndromic Surveillance: A Practical Guide Informed by the Early Experience. J Am Med Inform Assoc. 2003 Nov 21;11(2):141–50. doi: 10.1197/jamia.M1356. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Schiefelbein EL, Olson JA, Moxham JD. Patterns of health care utilization among vulnerable populations in Central Texas using data from a regional health information exchange. J Health Care Poor Underserved. 2014 Feb;25(1):37–51. doi: 10.1353/hpu.2014.0020. [DOI] [PubMed] [Google Scholar]
  • 19.Agency for Healthcare Research and Quality, US Dept of Health & Human Services. Statewide Health Information Exchange Provides Daily Alerts About Emergency Department and Inpatient Visits, Helping Health Plans and Accountable Care Organizations Reduce Utilization and Costs AHRQ Innovations Exchange.htm [Internet] 2014. Jan, Available from: http://www.innovations.ahrq.gov/content.aspx?id=3988.
  • 20.Shapiro J, Onyile O, DiMaggio C, Kuperman G. Validating Health Information Exchange Data for Quality Measurement. AMIA 2013 Annual Symposium; Washington, D.C.. 2013; [PMC free article] [PubMed] [Google Scholar]
  • 21.Garg N, Kuperman G, Onyile A, Lowery T, Genes N, DiMaggio C, et al. Validating HIE Data For Quality Measurement Across Four Hospitals. Society for Academic Emergency Medicine Annual Meeting; 2014; Dallas, TX. 2014. [PMC free article] [PubMed] [Google Scholar]
  • 22.Data Quality Collaborative, EDM Collaborative. DQC White Paper Draft 1: A consensus-based data quality reporting framework for observational healthcare data. [cited 2014 Apr 9]; Available from: http://repository.academyhealth.org/cgi/viewcontent.cgi?article=1001&context=dqc.
  • 23.Kahn MG, Raebel MA, Glanz JM, Riedlinger K, Steiner JF. A pragmatic framework for single-site and multisite data quality assessment in electronic health record-based clinical research. Med Care [Internet] 2012. [cited 2014 Apr 9];50. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3833692/ [DOI] [PMC free article] [PubMed]
  • 24.Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2013 Jan 1;20(1):144–51. doi: 10.1136/amiajnl-2011-000681. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Dixon BE, Siegel JA, Oemig TV, Grannis SJ. Electronic health information quality challenges and interventions to improve public health surveillance data and practice. Public Health Rep. 2013;128(6) doi: 10.1177/003335491312800614. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Kuzmak PM, Walters RF, Penrod G. Technical Aspects of Interfacing MUMPS to an External SQL Relational Database Management System. Proceedings/the. Annual Symposium on Computer Application [sic] in Medical Care Symposium on Computer Applications in Medical Care [Internet]; American Medical Informatics Association; 1988. pp. 616–20. [cited 2014 Apr 13]. Available from: http://europepmc.org/articles/PMC2245217. [Google Scholar]
  • 27.Health Level 7 [Internet] [cited 2014 Apr 17]. Available from: http://www.hl7.org.
  • 28.Grannis SJ, Overhage JM, Hui S, McDonald CJ. Analysis of a probabilistic record linkage technique without human review. AMIA annual symposium proceedings [Internet]; American Medical Informatics Association; 2003. p. 259. [cited 2014 Apr 13]. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1479910/ [PMC free article] [PubMed] [Google Scholar]
  • 29.Rudin RS, Simon SR, Volk LA, Tripathi M, Bates D. Understanding the decisions and values of stakeholders in health information exchanges: experiences from Massachusetts. Am J Public Health. 2009;99(5):950. doi: 10.2105/AJPH.2008.144873. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.LaCalle E, Rabin E. Frequent Users of Emergency Departments: The Myths, the Data, and the Policy Implications. Ann Emerg Med. 2010 Jul;56(1):42–8. doi: 10.1016/j.annemergmed.2010.01.032. [DOI] [PubMed] [Google Scholar]
  • 31.Althaus F, Paroz S, Hugli O, Ghali WA, Daeppen J-B, Peytremann-Bridevaux I, et al. Effectiveness of Interventions Targeting Frequent Users of Emergency Departments: A Systematic Review. Ann Emerg Med. 2011 Jul;58(1):41–52.e42. doi: 10.1016/j.annemergmed.2011.03.007. [DOI] [PubMed] [Google Scholar]
  • 32.Spillane LL, Lumb EW, Cobaugh DJ, Wilcox SR, Clark JS, Schneider SM. Frequent users of the emergency department: can we intervene? Acad Emerg Med. 1997;4(6):574–80. doi: 10.1111/j.1553-2712.1997.tb03581.x. [DOI] [PubMed] [Google Scholar]
  • 33.Stokes-Buzzelli S, Peltzer-Jones JM, Martin GB, Ford MM, Weise A. Use of health information technology to manage frequently presenting emergency department patients. West J Emerg Med. 2010;11(4):348. [PMC free article] [PubMed] [Google Scholar]
  • 34.Keith KD, Bocka JJ, Kobernick MS, Krome RL, Ross MA. Emergency department revisits. Ann Emerg Med. 1989 Sep;18(9):964–8. doi: 10.1016/s0196-0644(89)80461-5. [DOI] [PubMed] [Google Scholar]
  • 35.Wu C-L, Wang F-T, Chiang Y-C, Chiu Y-F, Lin T-G, Fu L-F, et al. Unplanned Emergency Department Revisits within 72 Hours to a Secondary Teaching Referral Hospital in Taiwan. J Emerg Med. 2010 May;38(4):512–7. doi: 10.1016/j.jemermed.2008.03.039. [DOI] [PubMed] [Google Scholar]
  • 36.Shapiro JS, Onyile A, Patel VR, Strayer RJ, Kuperman G. Enabling 72-hour Emergency Department Returns Measurement With Regional Data From a Health Information Exchange. Ann Emerg Med. 2011 Oct;58(4):S295. [Google Scholar]

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