Hello, everyone. I'm Lisa Anderson, the Project Director for eClinical in the Department of Quality Measurement at the Joint Commission. I would like to thank you for joining us for our Pioneers in Quality 2018-2019 Expert to Expert Series. Today we will be focusing on the PC-01 and PC-05 eCQMs. For participants that would like to use the Closed Captioning service, please see the link on this slide. This information is also accessible via a participant pane in the Go to Webcast platform.
The Joint Commission and CMS designed the Pioneers in Quality Expert to Expert Series to support hospitals adopting electronic clinical quality measures and transitioning to the new clinical quality language, or CQL. We introduced this series with a CQL Basics webinar followed by sessions covering the stroke, AMI, ED, VTE, CAC, and (inaudible) eCQMs. And based on participant feedback, we have extended the webinar sessions to 90 minutes to allow for a live Q&A session after each measure presentation. At the end of today's session, participants will be able to apply concepts learned about the new CQL expression language for PC-01 and PC-05. Identify their common issues and questions. And prepare to implement CQL for the 2019 eCQM reporting year for 2020 data submission. The slides are available in the Event Resources pane. Select the PDF, and you can download and print the slides. These sessions are designed to be interactive. The Questions pane permits participants to ask questions and view responses in real time. If possible, please reference the measure name and slide number in your question. Additionally, you can visit links or resources noted in the slides. Please note, a recording of today's presentation, the slide deck, and Q&A documents will be available on the Joint Commission website in several weeks. We hope you find this information helpful and share it with interested colleagues. CE credits are offered for all our Pioneers in Quality webinars. This webinar is approved for one Continuing Education credit for the Accreditation Council for Continuing Medical Education, American Nurses Credentialing Center, American College of Healthcare Executives. California Board of Registered Nursing, International Association for Continuing Education and Training, Certified Joint Commission Professionals Recertification Credit. CE, CME, CEU credits are available for the live audio only. Credits will not be available for webinar replays. To claim credit, you must have individually registered for the webinar, listened to the live webinar in its entirety. Only those listening live are eligible to receive credit. Completed a post-program evaluation.
The program survey link will be sent to participants' emails after the webinar. Printable certificates will be emailed to those eligible two weeks after the session. All participant CE certificates will be sent at the same time. If you are listening with colleagues and did not use your own link or phone line to join, you can still obtain CE credit if you meet these three criteria. An automated email after the session will provide information on how to access the survey. For more information on the Joint Commission's Continuing Education policies, please visit the link provided in the bottom of this slide. The following staff and speakers have disclosed that neither they nor their spouses or partners have any financial arrangements or affiliations with corporate organizations that either provide educational grants to this program or may be referenced in this activity: Lisa Anderson, MSN, RN-BC, Project Director, eClinical, Department of Quality Measurement; Susan Yendro, MSN, RN Project Director, Department of Quality Measurement; Mia Nievera, MSN, RN Associate Project Director, eClinical, Department of Quality Measurement; and Yanyan Hu, MS Associate Project Director, eClinical, Department of Quality Measurement. I am now going to turn it over to Susan and Mia to begin their presentation. Take it away, ladies. Thanks, Lisa. This is Susan Yendro. As Lisa said, I'm one of the Project Directors here at the Joint Commission. And the first measure that we're going to talk about is PC-01, Elective Delivery.
This measure is designed to identify patients with elective vaginal or cesarean deliveries at greater than or equal to 37 and less than 39 weeks of completed gestation. The American College of Obstetrics and Gynecology and the American Academy of Pediatrics have had a longstanding standard requiring 39 weeks gestation completed prior to doing an elective delivery. This has become - had become common practice to deliver early without a medical indication. And this was done mostly for convenience for either the provider or the mother. However, it resulted in significant short-term neonatal morbidity and higher neonatal intensive care unit admission rates. It also resulted in more cesarean births and longer length of stays for mothers. However, recently studies have shown support for successful reduction of early elective deliveries in many hospitals. In fact, the national rate for the chart (inaudible) measure, which were started to - were started to be collected in 2019 - or 2010, showed that nearly 14% of patients were delivered early. And that has re -reduced recently to less than two percent. But that - we also want to acknowledge that hospitals need to remain vigilant with this measure. And now I'll turn it over to Mia to continue the presentation. Thanks, Susan. And good morning, everyone. So, CQL is the new clinical quality language used to create the measure specifications. And so the goal today isn't to teach you how to write CQL, but to help familiarize you with the CQL terminology to help you read and understand the logic to better support your organization.
And so, if you've been following the series, then you know we will be technical in nature. But, again, with CQL, it's important to understand how the technical language impacts the clinical intent of a measure. So, let's go ahead and start with some of the CQL basics to set the foundation for today. This diagram shows the evolution of the standard to create eCQMs. And eCQMs consist of three components. First we have the metadata, which uses the Health Quality Measure Format, or the HQMF as the basic electronic specification for the measure. The second is the data model. And we use the Quality Data Model, or QDM for short, which defines the relationship between patients and clinical concepts in a standardized way. And lastly, we have the logic. So as of January 1, 2019, we made the transition from QDM to CQL. And that transition has been a big undertaking, but it does have its benefits. So from a technical standpoint, CQL simplified the logic making it easier for the system to compute. But more importantly, from a clinical perspective, CQL allows for more flexibility in the logic with better timing precision so that the logic can be better aligned with the clinical intent of a measure. This is a screenshot of a human readable. And for those that are new to eCQMs, a human readable is a file format of the measure specification that a person can read versus a computer. And the first thing I want to point out is if you look under the initial population, it only refers to a single line. TJC dot encounter with principle diagnosis and age. So this is a major difference from the QDM version where in QDM there were lines and lines of logic. Here with CQL, the logic is simplified into what we call definitions. And then we title these definitions using more natural language to capture the meaning of the logic it represents. The second thing I want to note are the definitions and functions section.
These are bracketed together and named as building blocks, which I'll talk about a bit more here in the next slide. But collectively, these are all the definitions used to build the population criteria here at the top of the page. So to give you a better understanding of the CQL structure and a CQL definition, this diagram depicts a visual representation of a very simple construct for the initial population. So if you imagine with me for a moment that you have a set of Legos. And if you've never played with them before, you're given a set of directions which instruct you on how to connect several blocks together in order to build a structure. Well, we can take that same concept into CQL where we have the clinical specifications as directions to build these definitions. These definitions, then, are like Legos, or building blocks, where we use one definition to build upon another until you have a completed structure. So looking here at the diagram, the definition we're building is the initial population, which we define as C. Now remember, each block represents a definition, and each definition has a name and logic to represent a population parameter. So in Block A, this is looking for a specific encounter type. For example, an inpatient or an ED encounter. You'll also want to note that A, being the largest block, is to represent the largest patient population. So the idea is as we start building the population criteria, we want to start narrowing down the patient population. So now in Block C, rather than repeating the encounter logic again, we can simply pull in definition A into the logic and then add the next criteria we're looking for.
So in this case, we're looking for a diagnosis. And this is how we connect the definitions together, by nesting one into the other. So then definition B now includes the encounter type and the diagnosis. So to complete the structure, we need an age block. So if we follow the same pattern where we connect B with age to create C. So hopefully this shows how we build off a definition so that each successive definition helps to further constrain the logic. So now here in C, we have all the criteria we've defined. The encounter type, the diagnosis, and the age of the patient. And that's how C becomes the initial population. So looking at this concept in more concrete terms, in this example we have three definitions to build the initial population. So starting with TJC dot nonelective inpatient encounter, it's looking for an inpatient encounter. Then in the next block, all stroke encounter, notice how it starts with the previous definition name, and then we add the diagnosis criteria. And then again in block - in the next block of encounter with principle diagnosis and age, we start with the previous definition and then add age criteria. So then finally, encounter with principle diagnosis and age is directly used as the initial population. And you can see how using this methodology of building blocks makes it cleaner to read and allows us to reuse the definitions throughout the measure specification. In the next slides we￢ﾀﾙll review some of the basic terms used in CQL. And so, as I mentioned, CQL uses definitions, and all definitions have a name. And we try to use a name that captures the meaning, or the meat, of the expression.
And an expression refers to the content of the definition, so, essentially, the logic. Now, remember, we can reuse definitions, and the way we pull them into the logic is - is by creating a library. And libraries can be created at the measure level, so each measure builds its own library of definitions. But we can also create global libraries which can be shared across all measures. And we use a library alias to identify which library the definition came from. As you can see, it's noted here, the prefix and the definition name. However, some definitions may not come from a specific library, so you may not see that denotation. Here now on slide 18, looking at the basic construct of an expression. Expressions use data type. Data type describe a part of the clinical care process which refers to a specific category in QDM. So, for instance, the encounter performed data type is in the encounter category. A medication administered data type belongs to the medication category. Each data type has their own set of attributes. Attributes provide specific details about their data type. So here in the example, dot relevant period is an attribute of the encounter performed. Meaning, the relevant period defines the start and end time of the encounter. Next we have a value set which specifies the kinds of data or codes that we're looking for relative to the QDM category. So ultimately, these are the codes the logic is looking for in the EHR. So in this example, the value set of nonelective inpatient encounters consists of a list of SNOMED codes. And to satisfy the logic, we would expect to see that any one of those SNOMED codes are in the patient's record. And lastly we have an alias. Aliases in expressions are used to give a source a name so that piece of logic can be referred to easily within the expression to avoid restating. And just briefly, here is a terminology section in the human readable where you can all the value sets and direct reference codes used in the measure. The Value Set Authority Center is where you would go to verify these codes using the unique object identifier, or the OID, number to search. So, one of the most common questions we get is what does "union" mean? And union is a CQL operator that combines two or more lists together. So in the diagram, any element in List A or List B will satisfy the condition. So, anything in red. From this you can understand why we've related "union" to meaning or because anything from A or B will meet. And just to clarify what lists are, lists are the result of what the logic is looking for in the EHR. So here in the example, the logic is looking for a diagnosis code of either perforation of uterus, or uterine window, or uterine rupture, or ectopic pregnancy. So what union does is that it combines all the diagnosis codes into one list so that if the patient encounter has any one of these diagnosis codes, it will satisfy the condition. Intersect is another common operator in CQL. Which is only looking for the common elements between List A and List B. So, the red depicts the shared elements between the two lists. To add a little more context, let￢ﾀﾙs look at the example. If List A returns a list of all inpatient encounters with age 18 years and older, and List B returns a list of all encounters with a CBC level, then the result of the intersection is a list of all inpatient encounters with age 18 years and older and a CBC level. Okay. So that takes us to the end of our CQL basics. Let￢ﾀﾙs transition back into the measure now here on slide 22 for CMS113 version seven.
And remember, the version number is important to identify the correct version of the measure being discussed. So the initial population reads, patient age greater equal to eight year and less than 65 years admitted to the hospital for inpatient acute care to undergo a delivery procedure and had a length of stay less than or equal to 120 days that ends during the measurement period. The definition for the initial population is delivery encounter with age range. And to build the initial population, we used two additional definitions. Encounter with age range, and global dot inpatient encounter, as highlighted here in red. The global dot inpatient encounter is the base definition.
And remember, global refers to the global library.
So this logic looks for an inpatient encounter. So a patient must have one of the encounter codes listed from the inpatient encounter - I'm sorry - the inpatient value set to meet the condition. And we use a CQL function length in days to calculate the inpatient encounter to be 120 days or less. The dot relevant period, remember is an attribute, that defines the start and end date time of the given timeframe. So the logic is saying that the relevant period for the inpatient encounter needs to be 120 days or less and must also end during the measurement period. And in the next definition, encounter with age range, we pull the global dot inpatient encounter and alias it as encounter inpatient to refer to it easily throughout the expression. And we use the function called calendar age in years at to calculate the patient's age at the time of the start of the encounter. So in other words, the patient needs to be eight or older and less than 65 by the start of the admission. Moving up to the delivery encounter with age range, we use the procedure performed data type to look for procedure codes within the delivery procedures value set to determine if a delivery was performed. And we use the attribute relevant period to look for the delivery procedure that starts during the encounter. So then, within the final definition, we're looking for encounters that satisfy the age criteria with a delivery procedure to qualify for the initial population. And the initial population is the pool of encounters that we draw from as we move through the measure algorithm. So, next in the process, in the measure algorithm, is the denominator.
And it reads, patients delivering newborn with greater than or equal to 37 and less than 39 weeks of gestation completed.
Now, in this definition we carry through all the definitions from the initial population into the denominator to build the qualifying encounter. So the definition, delivery encounter with age range, becomes the qualifying encounter to move through the measure algorithm. And we give it alias qualifying encounter, again, for the ease of reference throughout the expression. This expression also calls out another definition, estimated gestational age captured on the day of delivery. And starting with the base definition, gestational age near term, we use the assessment performed data type to look for a code from the estimated gestational age at delivery value set to determine if an assessment for gestational age was completed. The dot result is an attribute of the assessment performed data type which defines the gestational age to be greater than or equal to 37 weeks and less than 39 weeks. So moving up to the estimated gestational age captured on the day of delivery definition, we use the assessment performed data type with the time delivery value set to look for the time of delivery. The author date time is an attribute of the assessment performed data type, which is a time stamp of when the gestational age assessment was entered. And we use here the interval operator which defines the time period between start and the end date time. So this definition is looking for the gestational age to be documented on the same date of delivery but prior to the time of delivery. Now in the final definition, delivery encounter near term, we pull in all the criteria previously defined and add a timing condition which requires a gestational age result to be documented during the qualifying encounter. Okay, now in slide 34, and moving into the next measure process, the denominator exclusion reads, patients with conditions possibly justifying elective delivery prior to 39 weeks gestation. The definition delivery encounter with justifying conditions, we have two conditions that are connected using the union operator. So if a patient meets any of these two conditions, the patient will be excluded from the denominator. In the first condition, we pull in the qualifying encounter and use the diagnosis data type to determine if a patient has a diagnosis code in the conditions possibly justifying elective delivery prior to 39 weeks gestation value set. And the attribute prevalence period is a time period between diagnosis onset and abatement or end time. So we're looking for the diagnosis prevalence period that overlaps the delivery encounter. Here in the second condition, we're looking for the same diagnosis codes, but using the dot diagnosis attribute which is defined as an encounter diagnosis. So here the timing isn't important because it's tied - already tied to the encounter. So the denominator exclusion uses these two conditions in order to reflect varying workflows and how this information is captured.
Okay. Moving on to slide 38, in the numerator. And the numerator reads, patients with elective deliveries by either cesarean birth while not in labor and with no history of prior uterine surgery. Or, medical induction of labor while not in labor prior to this procedure. Again, by using union, if a patient meets any of these two conditions, they will be in the numerator population. So let's look at the first condition. And I wanted to point out that this definition has three conditions broken into three expressions using "with" and "without." In the first condition, we start with the base definition is in labor, which is looking for a labor assessment performed.
Then in cesarean birth while not in labor, we use procedure performed data type to determine if a patient had a cesarean birth. We then pull in the definition "is in labor", and alias it as labor, so now, using the attribute of author date time, the labor documentation should be 24 hours or less before the start of the cesarean birth. So putting it all together, the first condition is looking for encounters with cesarean birth where the patient has no labor and cesarean is relevant period starts during the delivery encounter. The second condition is without history of uterine surgery procedure. Again, union combines all these surgical procedures highlighted into one list so that if any of these procedure codes are captured in the EHR, they will satisfy the condition. And procedure must start before the start of the delivery encounter. The third condition is without history of uterine surgery diagnosis where, again, union combines all these diagnosis data types highlighted into one list so that if any of these diagnoses are captured, it will meet the condition. And since we're looking for the history of the diagnosis, we use a prevalence period attribute where onset must start before the encounter. So, in summary, the first numerator condition is looking for encounter with a cesarean birth where a patient has no labor or history of uterine surgeries. And the second numerator condition is medical induction while not in labor.
Again, we have the union operator, so either condition will - for the medication administered or the procedure performed, will satisfy the logic. So let's start with medical induction medication administered while not in labor. First, this definition uses a from, where, and return query structure. This structure is used for a multi-source query. So what that means is that there are multiple sources and relationships that need to be correlated to the logic.
So, in this logic we have to correlate three sources: the qualifying encounter, the induction medication, and the labor. The definition medical induction medication is looking for a medication administered as in either oxytocin or dinoprostone value set. And we have two timing relationships to consider. The first is looking for the labor to be documented during the delivery encounter. The second, the logic is looking for the induction medication to be administered during 24 hours or less before the labor author, date, time. Now on slide 53, we move into the second condition in medical induction while not in labor, which is about the procedure performed. So similar to medication administered logic, this definition uses the same from, where, and return structure but with the medical induction procedure as one of the sources. And this definition is looking for a procedure performed in either artificial - excuse me - artificial rupture of membrane or medical induction of labor.
And the relationships are looking for a labor that has documented - that was documented during delivery encounter and an induction procedure that was performed 24 hours or less before the labor author, date, time. So in sum, a delivery encounter satisfying either one of the medical induction conditions will be in the numerator population. And that takes us to the end of PC-01, and I will turn it over to Susan to introduce us to our next measure. Susan? Thanks. Okay, switching gears to look at a baby measure. This is PC-05, Exclusive Breast Milk Feeding. And this measure looks to see that the newborn was exclusively breast milk fed during their entire hospitalization. Exclusive breast milk feeding for the first months of neonate's life has long been a goal of organizations such as the World Health Organization, Department of Health and Human Services, AAP, and ACOG. There is Cochrane reviews and many studies that substantiate the benefits of exclusive breast milk feeding. And much of this evidence has focused on the prenatal and intrapartum periods as critical to the success - success of breast feeding, whether it's exclusive or any breast feeding. There are many organizations that are actively promoting this goal such as Healthy People, the CDC, and many others. And in the chart of (inaudible) measures, we continue to see there is great opportunity for improving breastfeeding rates as the national rates are - continue to be around 52%. So with that, I'm going to turn it back over to Mia to continue to discuss the breast feeding measure. Thanks, Susan. So, diving right in, the initial population reads, single newborns with an estimated gestational age at birth of greater than or equal to 37 weeks who are born in the hospital and who did not have a diagnosis of galactosemia, were not subject to parental nutrition, and had a length of stay less than or equal to 120 days that ends during the measurement period. The definition is single live birth encounter with gestational age 37 weeks or more. And to build the initial population, we used three definitions: single live birth encounter with galactosemia, parental - parental nutrition, sorry, single live birth encounter, and global inpatient encounter. Again, we used the global dot inpatient encounter which is looking for the inpatient encounter to be 120 days or less and must also end during the measurement period. In the single live birth encounter definition, we used "union" to look for either an encounter diagnosis or a diagnosis data type in the single live born - newborn in hospital value set. And the diagnosis data type is used. The prevalence period or the onset date, time must occur during the encounter. And moving up to the next definition, we pull in the single live birth encounter. And with three additional conditions by using the "without" and "where" statements. So the first "without" condition is looking for an encounter that does not have a diagnosis of galactosemia, where the diagnosis onset date, time occurs during the encounter. Again, we're looking for without. The second "without" condition is looking for an encounter that does not have a parental nutrition procedure performed where the procedure starts during the encounter. The third condition, using "where", is looking for an encounter - I'm sorry, where not - is looking for an encounter that does not have galactosemia as an encounter diagnosis. So, we then pull these definitions into the final roll up and alias it as the qualifying single newborn encounter.
And then use the assessment performed data type to look for an assessment of estimated gestational age at birth where the gestational age result is 37 weeks or more and was documented during the qualifying encounter. Moving on to the denominator here on slide 65, it reads, initial population. And we use the initial population definition because the denominator does not change from the initial population.
So we can simply call in initial population as the definition itself, which we've defined here in the yellow box.
So then the definition, single live birth encounter with gestational age 37 weeks or more, becomes a qualifying encounter to continue moving through the measure algorithm. Now moving to the denominator exclusions, it reads, newborns who were admitted to the neonatal intensive care unit, the NICU, who were transferred to an acute care facility, or who expired during the hospitalization. The definition is newborn to NICU or discharged to acute care facility or expired. And, we begin with the qualifying encounter and use the attributes dot facility locations and dot code to identify a neonatal intensive care unit. We also use the attribute of discharged disposition to identify an expired patient or discharged to an acute care facility. So, putting it together, if a newborn is in the NICU or expired or discharged to acute care facility, they will be excluded from the denominator. Now on slide 69, we move into the numerator, and it reads, newborns who were fed breast milk only since birth. The definition is newborns fed breast milk only since birth. Again we have our qualifying encounter with two additional conditions, using "with" and the "without" statements. So, in the "with" condition, the logic is looking for the qualifying encounter with a breast milk administration, and that the administration must also start during the encounter. In the "without" condition, we also use that same substance administered data type to look for the dietary intake other than breast milk where the administration must start during the encounter. So in sum, the numerator is looking for encounters with breast milk administration only, so without any other dietary intake. And that takes us to the end of our measure review. But before we move into the Q&A portion, I do want to do a quick review of some of the key resources. Here, the eCQI Resource Center is a one-stop-shop for eCQM-related information. It houses the various tools and resources such as the measure specifications, the technical release notes, which describe all changes made during the annual updates and the addendum. There are measure flows which help to understand how the data is processed. There are links to the QDM and CQL standards. As well as previously-recorded webinars and slide presentations. There is also a calendar where you can find additional educational sessions. The Value Set Authority Center is where we store all the value sets and direct reference codes used in the measures. So I do want to mention it does require a license, but it is free. Here, on the next slide, in number three, we are frequently asked about how to implement CQL.
And there is a presentation from HIIN from 2018. It's called Getting Started With CQL, Technical Implementation for Vendors. And that provides an overview of the CQL structure, the building of a CQL execution engine, using open source execution engines. And just to note, there is a newer version presented at HIINs this year that will be made available.
We don't have the details, so more information to come.
Items four and five are quick links that will take you to the JIRA site to submit questions or comments specific to CQL or any of the eCQM measures. And lastly here in six and seven are the links to the Pioneers in Quality portal where you can find all of our previously-recorded presentations, slides, and Q&A documentation. Thanks, everyone, and Lisa, it's all yours. Great. Thank you, ma-am. We will now move into the Q&A segment of the session. I'd like to introduce Yanyan Hu, the Measure Lead for these two measures, who will be answering your questions today. Please use the Question pane to submit a question. Please indicate the measure name and the slide number if applicable. We will answer as many questions as possible in the remaining time. All questions submitted will be addressed in a follow-up Q&A document that will be posted on the Pioneers in Quality portal. Need one minute to pull my window back up.
There we go. And this first question is actually for Susan Yendro, our Clinical Lead. Does unstable lie or breach presentation count as an exclusion prior to 39 weeks? No.
Malpresentations are not necessarily a reason to delivery early. And so in most cases those conditions can be managed through labor and delivery at the appropriate time. Thank you. Our next question comes from Margaret O￢ﾀﾙBrien (sp) at Shore Medical Center. What will the definition of NICU be? Is it the state designation? We have a special care nursery not state certified as a NICU, however providers tend to document NICU anyway. Thank you for your question. In the slide 66, in the CQL expression, NICU is expressed as a facility location as a attribute under the encounter performed. So - So if your organization is - has their - has that unit identified as a - as a NICU in their facility structure, then that would count as an exclusion. I hope that helps. This next question comes from Elizabeth Shuett (sp) from Northwest Community Healthcare. Is there a definition for labor? Must there be cervical changes? I'm going to send that one over to Susan Yendro to answer. So there must be documentation of labor in order for the patient to be considered to be in labor. And so whether there are cervical changes or not, there needs to be a documentation that shows regular contractions. Thank you. This next question comes from Charmaine Selda (sp) at El Camino Hospital.
In PT-01, if labor precedes C-section, then case does not meet the numerator? It depends on the la - the C-section - yes, overall, the answer is absolutely yes. The case will be - will - will not meet the numerator. So if the patient is in labor prior to the procedure, then the case is excluded. Thank you. Our next question comes from Anna Myer (sp) at NCH Healthcare System. On slide 35, Mia, can we go back to slide 35 please? Sure. Where can we find the most updated version of the diagnoses and conditions possibly justifying elective delivery prior to 39 weeks gestation? And just give us a second and we'll go back to that slide so we can all see. So, basically this is - you can find the most updated version of the value sets in NLM, the (inaudible) website.
So the website information is provided in the source (inaudible). Great. Thank you. This next question is from Lindsay Starkey (sp) at Lexington Medical Center.
For PC-01, we often have patients with a thin uterine segment but doctors only document within their progress notes in the office.
If they don't add the diagnosis code prior to admission encounter, even though it's a true diagnosis, it would exclude the patient but our reports are not able to count this. Why can't we add this after the delivery encounter even if it's true to exclude this patient? So, yeah.
So clinically a thin uterine segment would be considered a prior - a prior uterine surgery or a prior uterine diagnosis. So it would have to be identified within the - the timing and the - the three fields that you've identified in your organization to pull that piece of data. Right. So if their workflow includes them documenting it in the - in the doctor's office in a note but it's not in a discreet field, then that would not be able to codified to a code that matches the value set. Our next question, I'm not sure how to pronounce this name so I'm not even going to try, but thank you. Does the hospital have to be baby-friendly designated in order to meet the PC-05 measure? No, absolutely not. You can use any processes or procedures that your organi - that your hospital chooses in order to improve your breast feeding rates. Baby friendly is just one process or procedure that you can use. Thank you. This next question comes from Christine Emoid (sp) at Premier. Referring to the definition of labor, if documentation shows only regular uterine contractions but no cervical change, how does this meet the definition of labor? If a patient is then given Pitocin, would this be considered an induction of labor and fall out under PC-01 if there is no medical indication to deliver? No. Regular uterine contractions is considered to be a positive finding for labor. And, so, if there - if there's regular uterine contractions prior to the initiation of Pitocin, this would be considered augmentation rather than induction. All right.
Our queue is currently empty. We'll give about another minute or so if people want to ask some more questions. This presentation was a lot shorter than some of our previous ones just due to the not as much content or complexity to the measures. We have another question from Shelley Enda (sp) from the Alaska Native Medical Center. Her question says, I see for PC-05, the national average is hanging around 52%. What is the benchmark for this measure as you did state there is an opportunity for improvement? Yes. So the Joint Commission hasn't established an exact benchmark. However, we do believe that most organizations could - an achievable benchmark would be around 70%. So you see there is a great deal of improvement that could be operationalized by many hospitals. Thank you. Our next question comes from Susan McDonald (sp) from Lutheran Hospital. Regarding the question about thin uterine segment, if the office notes are scanned in, are we able to count that? So, unfortunately, if the documentation is scanned in, that￢ﾀﾙs not typically a discreet field that could get extracted for use in the eCQM. If someone took that scanned document and reentered the information and transcribed it into a discreet field in the EHR, that could count. But that - that kind of adds additional workload and is not typically found in the normal workflow. Okay. This next question comes from Irene from Uniontown Hospital. Will the questions and answers be on the slide deck also? So we - after we finish the presentation today, all the questions and answers will be transcribed into a separate document that is loaded on the Pioneers in Quality portal along with the slide deck and the recording once that is made available. And we'll be able to - in one of the future slides we have the URL to that site. And we just have a comment from Don Atley Carter (sp) from Well Star saying it's important to note that we are dealing with - I think it's important to note that we are dealing with what can be captured in discreet data fields. Providers must work with the EMR vendors to ensure their documentation can be captured. So, thank you, Don, for that comment. You're -you're right on that. Our next question comes from Alan Crawford (sp) from Banner Health. Off topic, but has CMS provided benchmarks for PC-06 yet? Yeah, this is Susan.
No, so this measure is - has just been launched by the Joint Commission. Has been used - in use for several years by the California Maternity Quality Collaborative. And you can look at their website for more information on what's been happening in California regarding benchmarks. Thank you, Susan. This next question is from Wanda Rogers (sp) from Memorial Hermann Healthcare System. Is there a plan to align measure definition of labor for PC-01 with ACOG and (inaudible) definition of labor? So, this is Susan.
So, definition of labor is - we definitely look to align with the national organizations such as ACOG and (inaudible). If there is something specific that you're looking for that you don't see there, please do forward that to us. Thank you. Yeah.
Just to add an additional note onto that. And currently value set is pretty much aligned with, you know, the ACOG, and just a little bit probably the chart abstract the measure, PC-01, the labor, might be a little bit different from the ACOG. So, yeah, if you have specific code or something you think is not aligned, please let us know. This next question comes from Eric Leaha (sp) from VCU Health. More general question. Any indications on CMS keeping PC-01 beyond 2020 and not retiring the measure? Will TJC keep this measure as an eCQM beyond 2020? Thank you very much for your question. So, in the final rule from CMS, they did indicate that they are removing the PC-01 eCQM from the 2020 reporting period. However, the Joint Commission is - is pursuing keeping that as a measure in our own portfolio. And more details will come on that about mid-summer. Okay. That was the last question in our queue.
We'll give another minute or so if folks have thought of more questions. Okay. Well, thank you every - We do have one more.
This is from Debra Ahinaoka (sp) from Capilano Medical Center. Is that in Hawaii? I'm super jealous. Do you have the national rate and benchmark for PC-02, 03, and 04? Yes. So, this is Susan again. We, as I said, Joint Commission doesn't necessarily set a - a benchmark for the measures. However, the national rates are reported in our national - our annual report. The most recent one is posted on our website. Thank you, Susan. All right. Well, it looks like questions are winding down, so we will end this session ahead of schedule. Any questions unanswered today require some additional review and will be addressed in the follow up question-and-answer document. A few closing remarks before we end the session. As a reminder, the slides are available for download now.
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