Improving Patient Matching with a Simple Plug-In Verato® Auto-Steward™ Is a Cloud-Based Plug-In That Injects the Power of Referential Matching Into Your EHR Or EMPI

Improving Patient Matching with a Simple Plug-in Verato® Auto-Steward™ is a cloud-based plug-in that injects the power of Referential Matching into your EHR or EMPI.

• Instantly and dramatically improve your EHR’s or EMPI’s patient matching without disrupting its core functionality.

• Get the full value from your EHR or EMPI investment and increase patient satisfaction and safety.

Accurate patient matching has Because it is cloud-based, Verato simultaneously become 10x more Auto-Steward does not require any challenging and 10x more important customization, does not disrupt any of than it was even just a few years ago. your EHR or EMPI’s core functionality, But conventional patient matching and is fast and easy to deploy. So technologies have reached their whether you use an EHR from Epic®, breaking points, and duplicate rates Cerner®, Allscripts®, eClinicalWorks®, are skyrocketing as a result—as are the athenahealth®, McKesson®, or another costs associated with having duplicate vendor—or whether you use an records and incomplete patient records. EMPI technology from IBM® Initiate™, QuadraMed®, NextGate®, or another The simple fact is whether you rely vendor—Verato Auto-Steward can on the patient matching technology improve your patient matching, built into your EHR, or whether reduce your duplicates, and reduce you’ve invested in an enterprise the costs required to resolve matches master patient index (EMPI) your EHR or EMPI cannot resolve. technology, your organization is suffering from rising duplicate rates that degrade care, increase costs, and impede revenue collection.

Luckily, there is a simple way to dramatically improve the patient matching of your EHR or EMPI. Verato® Automatically Auto-Steward™ is a cloud-based plug- resolve 50-75% in for EHR and EMPI technologies that uses a powerful new paradigm of the toughest in patient matching called Referential Matching to automatically resolve matches your 50-75% of the toughest matches your EHR or EMPI EHR or EMPI cannot resolve. These matches would otherwise have to be cannot resolve manually resolved by a data steward or health information management professional—or worse, remain unresolved duplicate records which represent risk and increase costs of care.

Copyright © 2018 Verato, Inc. 2 Improving Patient Matching with a Simple Plug-in Accurate Patient gaining this accurate and complete view has suddenly become nearly Matching Is Becoming impossible for conventional EHR and 10x More Challenging EMPI technologies.

and 10x More Critical In fact, no matter how much you The sheer amount of patient data have invested in your EHR or EMPI, stored by healthcare organizations is no matter how small your patient exploding. Large volumes of patient data are entering organizations UPICATE RATES IN HEATHCARE RGANIATINS from new sources and through new channels—such as newly acquired 0% 1% hospitals, newly consolidated EHRs, patient portals, smart medical devices, and personal health record apps. But each new source and dataset has its own 10% 10% data formats, data standards, and data governance rules. In short, there is much Average uplicate Rates uplicate Average more data, much more variance in the data, and much lower quality data—all combining to create an insurmountable 0% challenge for EHR and EMPI patient 00 01 Year matching technologies, which must -2018 Mid-Year EHR Consumer Satisfaction Survey, Black Book Market Research attempt to match and link all this data -2008 Quality Impact of the Master Patient Index, Journal of AHIMA to the correct patient identities. population is, no matter how clean your data is, and no matter how diligent This insurmountable challenge has your registration staff is, your EHR or come at the worst possible time. EMPI is riddled with duplicate records. Accurate patient matching is the According to a recent Black Book foundation of almost every critical Research survey, the average duplicate strategic initiative within today’s health rate within healthcare organizations systems—from patient engagement and is 18%,1 which is up significantly from patient experience initiatives, to clinical the national average of 8-12% just ten and quality analytics, to population years ago.2 health analytics, to health information exchange, to interoperability, and to improved revenue cycle management. Every aspect of a health system’s operations and care management 1 2018 Mid-Year EHR Consumer Satisfaction Survey, requires having an accurate and Black Book Market Research 2 2008 Quality Impact of the Master Patient Index, complete view of every patient. But Journal of AHIMA

Copyright © 2018 Verato, Inc. 3 Improving Patient Matching with a Simple Plug-in There Are Substantial— as patients gain visibility into their health records through patient portals and Growing—Costs and personal health record apps— to Inaccurate Patient because these portals and apps Matching also give patients instant visibility into any missing health records. The risks and costs of duplicate records are skyrocketing. According to the same Black Book survey, each duplicate The cost of record costs healthcare organizations over $800 per emergency department INACCURATE (ED) visit and over $1,950 per inpatient Patient Matching stay due to redundant medical tests and procedures. In addition to these costs, the survey found that 33% of all denied claims were a result of poor patient matching, costing the average 00 hospital $1.5M annually and the per emergency healthcare industry $2 billion annually. department visit A different study is not as optimistic, asserting that patient misidentification 10 costs the average hospital over per inpatient stay $17.4M annually in denied claims.31

All of these duplicate records also reflect 1.M poorly on healthcare organizations. annually in denied claims In fact, 88% of consumers directly blame the hospital system for their dissatisfaction with the lack of But most importantly, duplicate portability of their health care records can have drastic impacts records.42In an increasingly competitive on patient safety and care quality. healthcare marketplace, patient A survey of nurses, physicians, and satisfaction is a commodity—and IT practitioners found that 86% of today’s healthcare consumer is savvy these professionals have witnessed or enough to expect something as know of a medical error that was the simple as a complete and portable result of patient misidentification.53 health record from her provider. This Needless to say, this is unacceptable. is becoming even more important

3 2016 National Patient Misidentification Report, Ponemon Institute 4 2018 Mid-Year EHR Consumer Satisfaction Survey, 5 2016 National Patient Misidentification Report, Black Book Market Research Ponemon Institute

Copyright © 2018 Verato, Inc. 4 Improving Patient Matching with a Simple Plug-in The Fundamental can be left incomplete on a form, or Reason Conventional might be entered into a registration system with a default value (such Patient Matching as “01/01/1900” for a birthday). Technologies Fail Conventional patient matching technologies use sophisticated Conventional matching algorithms to compare the demographic technologies are only data of two records to determine as accurate as the whether the records belong to the patient demographic same patient. But the fundamental problem with these algorithms is data they are that they are only as accurate as the comparing—and underlying patient demographic data patient demographic they are comparing—and patient data is of notoriously demographic data is notoriously low quality. error prone, frequently incomplete, and constantly changing. These examples are just the tip of the Patients move and change addresses. iceberg, but they are representative Patients change their names after of the problem—a problem that is marriages and divorces. Some patients will not change or improve. Because prefer to use a nickname or a middle no algorithm—no matter how name instead of their given first name. sophisticated—can definitively Some patients have similar names determine that two health records to their parents, children, siblings, or belong to the same patient when those spouses. Some patients’ cultures have records have demographic data that is naming conventions that do not align errored, incomplete, and out-of-date. easily with registration forms—such as multiple last names that might EHRs and EMPIs Require be confused for middle names. Manual Review to Make Some demographic data is very Up to 30% of Matches ambiguous—like a Maryland Avenue If an EHR or EMPI cannot definitively address in Washington D.C. Some determine whether two similar records demographic data is recorded belong to the same patient, it will from a driver’s license, while other flag those records as a “task” for a demographic data is self-reported data steward or health information by the patient. And all demographic management professional to manually data is subject to errors and typos, or review and resolve. Each day, a typical

Figure 1: Data Stewardship Record Review Patient A Patient B Patient C

IENTIFIER 1234 4567 9876 NAME Kathy Smith Katherine Jones Cathy Jones GENER F F F B 1968-08-14 1968-08-14 1968-08-14 SSN 456-34-6547 456-34-6547 ARESS INE 1 123 Main St. 200 S Madison St. 123 Main St. INE Apt. #1 Apt. #1 CITY Spring eld St. Louis Spring eld STATE MO MO MO PHNE AREA 214 815 815 NUMBER 456-5643 987-4567 987-4567

mid-sized hospital can generate many a robust data stewardship program will hundreds of these tasks, outpacing the periodically suffer from massive backlogs capacity of the data stewards to process of tasks and spikes in the number of them—resulting in an ever-growing duplicate records in their systems. backlog of tasks and an ever-growing number of duplicates in the EHR or Referential Matching EMPI until they are eventually resolved. Is a Powerful New Organizations will often invest in large Technology that data stewardship programs in order Makes Matches EHRs to mitigate the costs of duplicate records, or will go through periodic And EMPIs Could “clean up” projects that attempt to Never Make address these backlogs all at once. Verato uses a new, powerful, and But thousands or millions of tasks will fundamentally different approach to be generated each time a new data patient matching called “Referential source is integrated, each time an Matching.” Rather than using algorithms EHR is migrated, and each time a new to directly compare the demographic patient population is incorporated data from two records, Verato compares from a recently acquired hospital. the demographic data from those Meaning that even organizations with records to its comprehensive and

continuously-updated reference demographic data than her record from database of identities. This database before her marriage. No conventional contains over 300 million identities patient matching technology would spanning the entire U.S. population, definitively declare these records to and each identity contains a complete represent the same patient. But most profile of demographic data—including technologies would flag the two nicknames, aliases, maiden names, records for manual review because they common typos, past phone numbers, contain the same SSN and birthdate, and old addresses. This database making them a potential match. The is essentially an “answer key” for two records would remain duplicates demographic data, and Verato uses in the EHR or EMPI until a data steward this answer key to make matches manually reviewed and resolved them. that conventional patient matching technologies could never make. By contrast, using the power of Referential Matching, Verato would As an illustration of the power of match each of these two records to the Referential Matching, consider two same Verato reference identity which records for the same patient, Kathy contains Kathy’s old and new names Smith. Since she last visited her and addresses [Figure 2]. Since both physician, Kathy got married, moved, records match to the same reference and changed her last name. So her identity, Verato would conclude current patient record contains different that they match to each other.

Figure 2: Verato Referential Matching Approach Verato Reference

Patient A AM Patient B athy Sith atherine ones AM AM athy Sith DA F BR 1968-08-14 atherine ones DA F BR DA F BR 1968-08-14 SS 1968-08-14 456-34-6547 SS SS 456-34-6547 P 456-34-6547 214 456-5645 P 815 987-4567 P 214 456-5645 815 987-4567 ADDRSS ADDRSS 123 Main Street ADDRSS 123 Main Street 200 S Madison St 200 S Madison St

Both match to the same reference record therefore they match to each other

Copyright © 2018 Verato, Inc. 7 Improving Patient Matching with a Simple Plug-in Verato Auto-Steward Below are brief explanations of how Injects the Power of to integrate with a few specific EHR and EMPI technologies, but Verato Referential Matching has already integrated with Epic®, into Your EHR or EMPI Cerner®, IBM® Initiate™, IBM MDM, Verato Auto-Steward is a cloud-based eClinicalWorks®, Mirth®, and OpenEMPI®, plug-in that integrates with your EHR and can integrate with any other or EMPI technology to resolve its technology through standard interfaces toughest matches using the power and HL7 messages. Integrations of Referential Matching. Verato Auto- can either run in real-time or can Steward automatically resolves 50- be scheduled as batch submissions, 75% of the potential duplicates that whichever is most appropriate. your EHR or EMPI has flagged as Epic tasks for data stewards or HIM staff to Use Epic® Clarity™ to create an “analytical manually resolve. This enables your report” of potential duplicate record organization to reduce duplicates, tasks from Epic® Identity™. Send reduce clinical costs, reduce the costs these tasks from your integration of data stewardship processes, improve engine to Verato Auto-Steward via care and patient safety, improve API. Verato resolves the tasks and revenue cycle, and get the most out returns its match determinations to of your EHR or EMPI investment. your integration engine. The resolved duplicates can then be merged in How to Integrate Epic using simple HL7v2 messages. Verato Auto-Steward Cerner Fundamentally, Verato Auto-Steward Extract the task list from the Cerner® receives pairs of potential duplicate EMPI as organized pairs of potential records via API call and returns its duplicate records. Send these tasks from match determinations for each your integration engine to Verato Auto- pair—either yes, no, or unknown. Steward via API. Verato resolves the tasks Importantly, Verato Auto-Steward and returns its match determinations to match determinations can always be your integration engine. The resolved sent through your existing business duplicates can then be inserted into rules—for example, not merging Cerner using CCL, where they will be duplicate records for a patient who available for review in the Cerner EMPI. is still in the hospital until after they IBM Initiate have been discharged. Verato match Extract the tasks from the IBM® Initiate™ determinations also include supporting EMPI via the Initiate Task Get APIs. Send demographic data evidence to show these tasks directly to Verato Auto- how each determination was made. Steward via the published API. Verato

resolves the tasks and returns its match determinations to the IBM Initiate EMPI. The resolution is then performed by the IBM Initiate Task Resolution API. Mirth Extract the task list from Mirth® Match (along with the corresponding demographics from Mirth Results) as pairs of duplicate records. Send these pairs from Mirth Connect to Verato Auto- Steward via API. Verato resolves the tasks and returns its match determinations to Mirth Connect. The resolution is then performed in Mirth Match and Mirth Results using applicable web services APIs offered by Mirth — (1) Merge patient by alias, (2) Link Identity.

Figure 3: Proven Resolution Rate with RESOLUTION RATE OF POTENTIAL DUPLICATE TASKS Verato Auto-Steward WITH VERATO AUTO STEWARD

90% 80% 70% 60% 50% 40% Resolution Rate 30% 20% 10% 0% Epic Cerner IBM IBM IBM IBM OpenEMPI Mirth Mirth Initiate Initiate Initiate Initiate

Copyright © 2018 Verato, Inc. 9 Improving Patient Matching with a Simple Plug-in You Have Already sophisticated, highly tuned algorithms Invested in Your EHR or to make matches your EHR could not make, and to help you manage patient EMPI. Get the Full Value information across EHRs and facilities. of Your Investment with Yet no matter how sophisticated and Verato Auto-Steward. well-tuned your EMPI is, you have been forced to make a terrible choice: You have invested millions of dollars in either invest in a large data stewardship your EHR and potentially millions more program to make matches your EMPI in your EMPI. You had to build an entire cannot make, or suffer the costs of rising business case around each investment duplicate rates. and then you picked vendors, purchased the technologies, and have since spent Verato Auto-Steward is the solution to untold amounts of money, effort, your patient matching challenges. By and time deploying, integrating, and automatically resolving 50-75% of your operating them. You have established EHR or EMPI technology’s potential workflows and business rules and match tasks, you will greatly increase complex processes that make them all patient matching accuracy, decrease work. But the truth is neither technology duplicates, improve your backlog review is working as promised. throughput, and re-focus data stewardship teams and HIM staff on Your EHR’s weakest link is its patient more important business initiatives. matching capabilities, yet these More importantly, by reducing your capabilities are foundational to every duplicate rate, you will also reduce other function of your EHR. Without the liability associated with duplicate accurate patient matching, your EHR’s records and improve safety and population health, health information satisfaction across your organization as exchange, interoperability, managed well as for patients. care, and revenue cycle management functions are severely inhibited. But You have already invested in your EHR more importantly, your EHR cannot and EMPI—now you can get the most assemble a complete and accurate view out of that investment. Verato Auto- of each patient’s health across clinical, Steward lets you instantly and specialty, lab, imaging, and pharmacy dramatically improve your EHR or EMPI records. This drastically impacts your with better patient matching and organization’s ability to provide care and without having to disrupt that ensure patient satisfaction and safety. technology’s core functionality. With Verato Auto-Steward, you can finally And your EMPI was supposed to solve ensure your world-class EHR or EMPI your patient matching challenges once technology has world-class patient and for all. It was supposed to use matching, too.