one to many biometric patient ID systems are the only way to prevent duplicate medical records

The Difference Between 1:N, 1:1, and 1:Few and Why it Matters in Patient ID

one to many biometric patient ID systems are the only way to prevent duplicate medical records

Did you know that not all biometric patient ID systems have backend matching types that can prevent duplicates, eliminate medical ID theft, and improve patient data integrity? Only 1:N biometric matching has this capability.

The following guest post was submitted by Joe Kubilius, Director of Product and Process Management at RightPatient®

Understanding the Digital World

In a world rife with digital devices and electronic gadgets, few of us probably know or understand how they actually work. Think about a smartphone for example – myriad buttons, switches, cameras, lights, and sounds instruct us to swipe this, or press that and we oblige. After all, the complexity of the backend processor and sensor network that makes these devices do what they do is probably of little interest to most of us, myself included.

Instead what we focus on is the end result – what you see, hear, and experience when you use a digital device. Few would argue that it’s necessary or even mandatory to have a thorough understanding of backend functionality on any digital device in order to appreciate the value it brings to our lives. For biometric patient identification solutions, this is definitely not the case.

Why Biometric Patient ID Technology is Different

Understanding biometric matching types is critical when selecting a patient identification solution. Most of us probably see biometric matching as rather black and white — for example, you place your finger on a fingerprint reader, a backend software program recognizes and verifies your identity, and you are on your merry way. The problem is that backend biometric matching technology is not cookie cutter and there are different matching types that carry different capabilities.

Why is this important to know and understand? We know that most healthcare organizations invest in the use of biometric patient ID solutions to increase patient safety by:

  • Eliminating medical identity theft and fraud at the point of service
  • Preventing duplicate medical records and overlays
  • Achieving and sustaining patient data integrity
  • Safeguarding personal health information (PHI)
  • Identifying unconscious or unknown patients

What most people don’t realize is that depending on which biometric matching type you select, achieving these goals is not 100% attainable with select patient ID solutions. The ONLY way to achieve the bulleted objectives is to implement a system that, during patient enrollment, compares a patient’s stored biometric template against ALL stored templates in the biometric database. If the ultimate goal is to improve patient safety and patient data integrity, only a one-to-many (1:N) biometric matching type can accomplish this.

Let’s take a closer look at the available biometric matching types and what they have the ability to do.

Understanding the Differences Between Biometric Matching Types

Biometric matching types can be categorized as: One-to-many or “Identification” (1:N), one-to-one or “Verification” (1:1), and 1:Few Segmented “Identification” (1:Few). Here is a breakdown of each matching type and how to interpret their capabilities:

  • (1:1) Verification: 1:1 biometric “verification” matching authenticates a patient’s identity by comparing a captured biometric template with a biometric template pre-stored in a database. 1:1 biometric matching rejects or accepts a patient’s identity but before the comparison takes place, hospital staff must first input a personally identifiable credential (e.g. – a date of birth, gender, etc.) prior to comparing a stored biometric template against a live scan. This personally identifiable credential points to a specific enrollment template in the database so using a 1:1 matching type answers the question, “Is a patient who they claim to be.”

Example: A patient walks into the ED. Hospital staff asks the patient for their date of birth then scans the patient’s biometric credential to compare it against the stored template for that patient to verify that the patient is who they claim to be. With 1:1 biometric matching, the registrar has to retrieve a patient’s medical record first. Assuming the patient has been previously enrolled, they then scan their biometric and the system compares the captured data only against the data on file for that medical record.

Takeaway: 1:1 biometric verification is beneficial for verifying a patient’s claimed identity but since it does not search the stored biometric template database in its entirety, it does not have the ability to prevent medical identity theft or fraud at the point of service nor does it have the capability to identify an unconscious or unknown patient since a personally identifiable credential is needed prior to conducting the biometric scan.

  • (1:Few) Segmented Identification: 1:Few biometric matching compares a patient’s captured biometric template against a segmented portion of the entire biometric database, therefore a personally identifiable credential must be provided prior to the biometric scan to determine the subset of biometric templates to compare against. For example, a patient would provide a date of birth prior to the biometric scan and a 1:Few segmented identification system would then compare that patient’s biometric template only against the templates that share the same date of birth.

Example: A patient arrives at a medical facility for treatment. At registration, hospital staff asks the patient for their date of birth which segments the biometric database to only those records that share the same date of birth and then captures the patient’s biometric credential for comparison against the segmented database.    

Takeaway: 1:Few segmented identification does not have the ability to search an entire biometric database in real time to prevent the creation of duplicate medical records or eliminate medical identity theft or fraud at the point of service. What if a patient attempting to commit fraud had previously enrolled their biometric credentials and it was linked to another electronic medical record, then returns to the medical facility claiming another identity and providing a falsified, different date of birth? Hospital staff would then link that patient’s biometric credentials to another electronic medical record and a 1:Few segmented identification matching type would not be able to catch the fraud or prevent a duplicate medical record for this patient. In addition,  if a patient arrived unconscious without any identification credentials in the ED, 1:Few segmented identification does not have the ability to identify them because a personally identifiable credential is required. How would an unconscious, unknown patient be able to provide this? Biometric patient ID matching systems based on 1:Few segmented identification do not have the ability to identify unconscious/unknown patients.

  • (1:N) Patient Identification: A one-to-many (1:N) biometric identification matching system instantly compares a patient’s captured biometric template against ALL stored biometric templates in the system. No other information is required from the patient other than their biometric credentials and this matching type represents the only true de-duplication mechanism and the only way to prevent duplicate medical records to achieve and sustain patient data integrity. 1:N biometric mathcing types ensure that once a patient enrolls, it is impossible to create a duplicate medical record for that patient.

Example: A patient arrives at a hospital for outpatient surgery. At the registration desk, hospital staff takes a patient’s photo with an iris recognition camera. The backend software instantly compares that patient’s biometric credentials to every single stored biometric template in the database.

Takeaway: 1:N biometric matching is the only true way to prevent duplicate medical records and overlays and eliminate medical identity theft and healthcare fraud at the point of service. By searching the ENTIRE biometric enrollment template database, hospital staff ensures that a patient has not tried to claim another patient’s identity, and is able to access the only electronic medical record linked to that patient with confidence. If a healthcare organization seeks to improve and sustain patient data integrity and patient safety, 1:N biometric searches are the only way to accomplish this. In our 1:Few example above, if an unconscious, unknown patient arrived in the ED and a hospital had implemented a biometric patient ID   system with 1:N matching, hospital staff would only need to capture the patient’s biometric credential for accurate identification.

Understanding the capabilities and limitations of biometric matching types is key to select a biometric patient ID system that will accomplish the goals of improving patient safety and patient data integrity in healthcare. Take the time to ask the right questions when evaluating a biometric patient ID solutions so you won’t be left in the dark about what a solution can and can’t achieve.

Have you implemented a biometric patient ID system based on 1:1 or 1:Few segmented matching type and did not understand the limitations? Please share your comments and feedback below!

Director of Product & Process ManagementJoe Kubilius is Director of Product and Process Management with RightPatient®. With over 10 years of experience in the design, development, and implementation of biometric identity management solutions, Joe has been integral to the success of hundreds of large and small scale deployments across the globe.

accurate patient identification in healthcare discussed during 09/11/15 #HITs,m tweet chat

#HITsm Tweetchat Highlights Progress, Obstacles for Patient Identification in Healthcare

accurate patient identification in healthcare discussed during 09/11/15 #HITs,m tweet chat

Healthcare professionals from around the world discussed the dilemma of achieving accurate patient identification in healthcare during the 09.11.15 #HITsm tweetchat. (Photo courtesy of HL7 Standards)

We had the honor of hosting the weekly #HITsm Twitterchat this past Friday centered on the topic of patient identification in healthcare. For those not aware of the #HITsm weekly chat, the HL7 Standards Web site deftly describes it as:

“…#HITsm is an acronym for “healthcare IT social media” and we focus on current topics that are influencing healthcare technology, health IT, and the use of social media in healthcare.” (courtesy of: http://www.hl7standards.com/hitsm-chat/)

The chat demographic spans a wide range of healthcare professionals — vendors, doctors, patients, industry experts, and even national professional healthcare organizations — all coalescing for one hour each week to discuss a wide range of topics related to health IT. 

Considering the importance of establishing accurate patient identification in healthcare and it’s ripple effect to many other aspects of care delivery, interoperability, accountability, and improving individual and population health, we selected this as a topic for the weekly discussion. Included in the discussion was an evaluation and assessment of the College of Healthcare Information Management Executives’ (CHIME) national patient identity challenge to determine if it will actually produce a solution that will eradicate the burdens of matching patient data and bring us closer to establishing higher data integrity, reducing medical errors, and improving care.

Topics for the chat included:

1. Establishing a national patient identifier continues to be a hot debate – will it ever materialize? Are there tangible benefits to a national patient identifier?
2. Will @CIOCHIME’s national patient ID challenge eradicate the burdens of matching patient data and bring us closer to establishing higher data integrity, reducing medical errors, and improving care?
3. Will true healthcare interoperability ever be realized in the absence of fixing the problem of inaccurate patientID?
4. Do patients now have an inherently larger role to ensure the accuracy of their medical records?
5. Does the explosion of mobile devices, telehealth, & mhealth tools raise the urgency level for healthcare providers to implement stronger patient ID technology?
6. What new patient identification technologies show promise to help improve patient matching and interoperability?

Responses, comments, and opinions to these topics were insightful and intelligent. Here is a list of select responses and feedback to some of the topics:

Topic 1: Establishing a national patient identifier continues to be a hot debate – will it ever materialize? Are there tangible benefits to a national patient identifier?

Topic 2: Will @CIOCHIME’s national patient ID challenge eradicate the burdens of matching patient data and bring us closer to establishing higher data integrity, reducing medical errors, and improving care?

Topic 3: Will true healthcare #interoperability ever be realized in the absence of fixing the problem of inaccurate #patientID?

Topic 4: Do patients now have an inherently larger role to ensure the accuracy of their medical records?

Topic 5: Does the explosion of mobile devices, telehealth, & mhealth tools raise the urgency level for healthcare providers to implement stronger patient ID technology?

          T5: Again, use case specific. Many of the use cases today won’t benefit from national pat id… BUT… not say to they won’t #HITsm

Topic 6: What new patient identification technologies show promise to help improve patient matching and interoperability?

           #HITsm T6 In healthcare, I would expect less fraud w biometric ID’s. Solves unconscious patient problem and the form time problem. #HITsm

A copy of the complete chat transcript can be found here.

As you can see from the selected tweets, the topic of achieving accurate patient identification in healthcare is not easy especially in the context of identifying a solution that can be used as a universal credential. We were pleased that the #HITsm tweet chat participants provided pragmatic, intelligent comments and insight to our questions demonstrating their keen ability to identify the root causes of achieving a national patient identifier and their tacit support for CHIME’s contest and prowess to thrust this complex issue into the national spotlight.

Our thanks to @OchoTex and @michenoteboom for providing us the opportunity to host their weekly #HITsm chat on patient identification in healthcare and for all of the healthcare professionals who participated!

tweet chat on patient identification in healthcare

RightPatient® to Host September 11th #HITsm Chat on Patient Identification in Healthcare

tweet chat on patient identification in healthcare

Join us on Friday, September the 11th for the #HITsm tweet chat where we will discuss CHIME’s national patient identifier challenge and the state of patient identification in healthcare.

Join us this Friday from 11 a.m. to 12 p.m. CST as we host the weekly #HITsm tweet chat and discuss the conundrum of establishing accurate patient identification in healthcare. The College of Healthcare Information Management Executives (CHIME) recently issued a $1 million “national patient ID challenge” in an effort to find a universal solution for accurately matching patients with their health information. 

Patient identification in healthcare has bubbled to the top of the health IT priority list since accurately identifying patients is perhaps the most fundamental element of ensuring data accuracy throughout the care continuum but a confluence of barriers has inhibited advancing initiatives intended to improve it and help increase patient safety. 

During the chat we will discuss CHIME’s initiative and debate on whether it truly has the ability to improve patient identification in healthcare plus we will talk in-depth about how inaccurate patient ID effects healthcare interoperability, the evolving patient role to ensure the accuracy of their PHI, new patient touchpoints that complicate the goal of implementing technology that improves identification accuracy, and which new patient identification technologies show promise to help advance accurate patient identification in healthcare.

Please join the discussion beginning Friday at 11 a.m. CST, 12 p.m. EDT by following the #HITsm hashtag.

Additional information about the topic and discussion can be found here.

What additional topics would you like to see added to the conversation on establishing accurate patient identification in healthcare?

iris biometrics for patient identification can help to identify unknown or unconscious patients

Novant Health Uses Iris Biometrics to Identify Unknown Patient

iris biometrics for patient identification can help to identify unknown or unconscious patients

Novant Health recently used the RightPatient iris biometrics patient identification system to identify an unknown, disoriented patient. (Photo courtesy of pixabay: http://bit.ly/2ibpdIi)

It’s a familiar case. An unconscious or unknown patient arrives in the ER without any identification leaving clinicians to administer care in the absence of any medical history to review. This presents a serious patient safety risk since treating an unknown patient without the benefit of securing their identity is dangerous and can be a huge liability. What if they are allergic to a certain medication? What if they have a pre-existing condition that must be considered prior to receiving any treatment?

Since these cases are more often trauma related requiring immediate attention, clinicians must take a risk and administer care in the absence of any historical medical data. An obvious threat to patient safety and a situation that clearly raises liability, healthcare organizations have long sought to adopt technology that can instantly identify patients in these cases without the need for any demographic information. 

The staff at Novant Health decided to proactively implement an iris biometric identification system throughout their network as a means to secure accurate patient ID and ensure that patients, no matter what the circumstances, are kept safe throughout the care continuum. Although adopting a biometric patient identification system to identify unconscious or unknown patients wasn’t the sole reason that Novant implemented this technology, they knew that by choosing to use iris recognition as their primary biometric modality they would be able to quickly and accurately identify any patient in these circumstances without having to ask for an additional identification credentials (e.g. – D.O.B.). 

Novant’s iris biometric patient identification system was recently put to the test when a disoriented, unknown patient arrived in the ER without any identification credentials. Novant staff quickly realized that they could take the patient’s photo with a RightPatient iris camera and if they had been previously registered in their Epic EHR database, the biometric patient identification system would recognize them and immediately pull up their medical record. Fortunately, the patient had previously been enrolled with the RightPatient system and their identity was instantly recognized after their photo was taken with the iris camera. A big relief to Novant staff since they were now able to not only access her medical history prior to treatment, but they were also able to quickly contact the patient’s relatives to inform them of the situation.

A link to the Novant article detailing the experience of using iris biometrics to identify an unknown patient can be found here.

Thank you to our partners at Novant Health for sharing this story and demonstrating the value of using biometrics for patient identification in the context of keeping patients as safe as possible throughout the care continuum!

How often do you experience situations where patients arrive at your facility without identification credentials? Did you know that not all biometric patient identification solutions have the ability to identify unknown or unconscious patients?