Applying Cost To Clinical Quality Measures

With the Kamira research project that I am leading, our team was able to instrument the Meaningful Use (MU) Stage 2 Clinical Quality Measure (CQM) logic with the procedural logic for calculating the results of the CQMs via the popHealth project.  What this popHealth CQM calculation software provided the Kamira team was the clinical building blocks of the CQM logic, which we were able to marry with publicly available claims data hosted by CMS based on de-identified real-life claims records.

With the CQM calculation land this collection of claims records, we were able to asses ranges of the cost associated with addressing the MU Stage 2 CQMs.  With this, we were able to provide a guess for how much cost would be introduced into the healthcare system if a provider were to attempt to address the numerator logic of an MU Stage 2 CQM.

Consider the MU Stage 2 CQM "NQF 0062: Diabetes Urine Protein Screening".  This CQM measures the percentage of patients 18-75 years of age diagnosed with diabetes who had a some form of urine screening during the measurement period.  However, if you look at the conditional logic of this CQM, you can see that having the dialysis procedure would 

Procedural numerator logic for "NQF 0062: Diabetes Urine Protein Screening"

When we applied the CMS claims data, you can see the wide range of costs associated with this particular CQM's numerator logic, spanning microalbumin testing, ACE inhibitor, and access to dialysis:

Numerator costs on NQF 0062 - Diabetes Urine Protein Screening

As a non-clinician, the first two tests in the numerator logic make sense to me (microalbumin lab tests).  They appear to be in the "spirit" of this CQM, measuring if the provider has performed the microalbumin lab test for kidney damage.  However, the Kamira automated cost assessment highlighted that the vascular access for dialysis is clearly a much more expensive procedure to address this numerator logic than the lab test.  We didn't have any claims data on the kidney transplant from our sample set, but I can only speculate that the cost for that claim would be in the six figure range.

Looking purely at the CQM logic, and not applying any clinical perspective on this CQM, the vascular access for dialysis procedure is viewed as equivalent to the microalbumin lab test, at least within the scope of in this CQM logic.  Both are equal when assessing if the provider is performing the best quality of care for their population of diabetic patients.  Again... a kidney transplant is also in that same category as being semantically equivalent to the microalbumin lab test!!!

On the flip side, it appears to me that vascular access for dialysis is meant to address a known clinical problem, whereas the microalbumin lab test is designed to only collect and present information to a provider.  This difference in the clinical purpose of the clinical activities is not considered in the CQM logic.  Again, the CQM logic views both as equivalent when measuring the quality of care that a provider is applying to their population of patients.

Based on the Kamira automated CQM cost analysis, my recommendation is to groom the logic of "NQF 0062: Diabetes Urine Protein Screening", and re-position the dialysis procedure probably belongs in the exception or exclusion logic, vs. the current numerator logic.  There are some additional opportunities to apply this cost consideration on all Meaningful Use Stage 3 CQMs.  By applying these cost metrics to the MU Stage 3 CQMs, at minimum, the policy makers could discuss the impact of cost with the numerator logic clinical data.  Additionally, these conversations could potentially lead to streamlining and pruning the MU Stage 3 CQM logic to avoid noisy and high variance healthcare costs within the logic of one CQM.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. © Rob McCready, 2013.

Thoughts on Clinical Quality Measure and Clinical Decision Support Convergence

I have been tracking several discussions for the past year to consider the convergence of Clinical
Quality Measures and Clinical Decision Support within Electronic Health Record systems.  As a quick refresher on the two types of healthcare reports:

Clinical Quality Measurement (CQM)

• Typically post-event reporting on treatment, usually via a quarterly or annual basis
• Longer term impact on healthcare quality of a population of patients

Clinical Decision Support (CDS)

• Guide clinical choices during treatment now
• More direct and immediate impact on the quality of care being provided to a patient

CQMs and CDS are clearly very closely related disciplines.  In fact, they are two sides of the same coin.  They both consider what actions providers take with the "hand they are dealt" with their patients.  For both types of reports, data in input via electronic clinical data, and both are implemented within EHR systems.  Additionally, the logic of a CDS rule and a CQM report are indeed very similar.

With CQMs, you are reviewing actions taken in the past to help educate and influence decisions in the future.

With CDS, you are working in real-time... in the present.

To put this in another context, with CQMs you may be viewing data that may have led to the death of a patient.  The goodness or badness has been performed by the provider or healthcare team.  With CDS, you have the potential to actually kill a patient by taking the wrong action.

This is the part of the CDS space that I personally find troubling to work with, since there is a much more real risk to harming a patient with either a bug in CDS logic or with ambiguity in standards that could be used to express CDS

Consider a Warfarin CQM that measures “Did you perform appropriate warfarin dosing?”  You could apply this CQM to a population of cardiac patients and consider the results in aggregate to see if over the course of a year.  Now consider a CDS that determines "What is the lethal dose of warfarin dosing that I am providing this patient... right now?!?”

For me, addressing the second scenario become really serious very quickly.  For healthcare providers, I do not think that they are phased with either domain, since they deal with life and death scenarios all the time.

But again, for me, these are the scenarios and domains where I lose sleep after work.

Several considerations have been discussed to expand the Health Quality Measures Format (HQMF) XML standard that is used for expressing the CQM logic for the Meaningful Use program.  The HQMF is an HL7 standard for representing a Clinical Quality Measure (CQMs) in XML.  The HQMF standard is a declarative approach to defining procedural CQM reporting logic.  What the HQMF standard has going for it, is (regrettably) being named the authoritative source for expressing the logic for the Meaningful Use Stage 2 CQMs.

The HQMF implementation of CQMs defines the data elements referenced by the logic, and associates them to value sets using object identifiers (OIDs).  These OIDs can then be used with the National Library of Medicine's (NLM's) Value Set Authority Center (VSAC) to translate clinical concepts into various operational clinical codes.

For those really interested in using the HQMF, you can download the HQMF files for all the Meaningful Use Stage 2 Clinical Quality Measures from the CMS site here.

Having worked with the HQMF XML standard indirectly for the past 2 years on both the popHealth project, and for the past year the Cypress project, my team has repeatedly expressed concern over the standard due to ambiguity and difficulty in ensuring that the HQMF standard is properly implemented in an EHR.  For our team, our challenge was consuming HQMF XML and turning it into procedural logic for Cypress and popHealth.

Based on the challenges my team has faced, I am very cautious about any considerations to re-use the HQMF standard for CDS support, since I feel it does a poor job expressing even the CQM logic.  My primary concern with the plans on converging CQMs and CDSs logic is the current declarative approach used for CQMs which is the HQMF XML standard.

What I feel is needed is a procedural definition of both CQM and CDS logic.  The reason for this need for procedural expression of the logic is that it allows for more rigorous testing, and also reduces the ambiguity associated with EHR systems have to implement their own parser to convert the declarative HQMF definition of either a CQM or CDS into their own procedural set of software instructions.

Consider the following procedural JavaScript logic built into popHealth v1.4 for the Meaningful Use Stage 1 CQM, NQF 0421 Adult Weight Screening:

function() {
  var patient = this;
  var measure = patient.measures["0421"];
  if (measure == null)
    measure={};

  var day = 24 * 60 * 60;
  var year = 365 * day;
  var effective_date = <%= effective_date %>;

  var measurement_period_start =  effective_date - (1 * year);
  var latest_birthdate = latestBirthdayForThisAge(65, measurement_period_start);
  var earliest_encounter =        measurement_period_start - year;

  var population = function() {
    var correct_age = patient.birthdate <= latest_birthdate;
    return (correct_age);
  }

  var denominator = function() {
    return inRange(measure.encounter_outpatient_encounter,
                   earliest_encounter, effective_date);
  }

  var numerator = function() {
    return weight_numerator(measure, 22, 30);
  }

  var exclusion = function() {
    return weight_exclusion(measure, earliest_encounter, effective_date);
  }

  map(patient, population, denominator, numerator, exclusion);
};

By having a single procedural definition for both CQMs and CDS, we should be able to have a higher level of confidence in the performance of the EHR systems that have to implement either a CQM or CDS.  What could then be done, is test and certify EHR systems ability to process the procedural definition of the standard, and then have a higher level of confidence that the any new CQM expressed in procedural logic... as long as it were syntactically well formed... would work with an EHR system.

The benefit to this type of approach is accelerated velocity in development, testing, and deployment of new CQMs and new CDS reports/rules.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. © Rob McCready, 2013.

The Four Different Types of Clinical Quality Measures

Clinical Quality Measures (CQMs) have been a significant part of my work for the past 3 years.  My first foray into the CQM space was with the popHealth project, which is a reference implementation of the CQM logic that a provider or EHR vendor could use or incorporate into a software project.  popHealth was entirely focused on a family of CQMs called "proportion-based".

While the "proportion-based" CQMs get the majority of the attention with the healthcare community, it was not until a year working on popHealth that I discovered that there are a total of four different types of CQMs that affect how the CQM logic is implemented and reported.

The four different classifications of CQM logic are:

Proportion

• This is the type of CQM that most individuals are familiar with when referring to the Meaningful Use program.  
• These types of CQMs are routinely referred to as the "Numerator/Denominator" CQMs.  I recently wrote about the exception and exclusion logic, but it is worth noting that those CQM reporting characteristics are only applied to proportion-based CQMs.
• Usually, the proportion-based CQMs are a positive measurement of quality, meaning that usually, the higher the value of the Numerator/Denominator proportion, the better you are doing as a healthcare provider
• Example: "What percentage of women over the age of 45 and under the age of 65, who have had an outpatient encounter in the past 2 years, have had a mammography screening?"

Continuous Variable

• These CQMs are usually applied in the hospital CQM domain.  
• These types of CQMs measure the average time 
• Example: "What is the average time for Emergency Department (ED) admission until either Discharge or Admission to Inpatient Hospitalization?"

Episode of Care 

• These CQMs assess each distinct ‘encounter’ between a patient and a provider, during a a measurement period.
• A single patient can contribute to numerous considerations of the CQM result if they had numerous encounters
• Examples: "Did the provider measure the patient’s blood pressure during a particular episode?" or "Were heart attack patients discharged with an Rx for Aspirin during a particular episode?"

Longitudinal

• These CQMs take into account complete patient record with focus on a ‘measurement period’
• Examples: "Have patients who turned 2 years old during the measurement period received all required vaccinations on schedule?" or "Have diabetic patients received 2 foot exams during the measurement period?"

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. © Rob McCready, 2013.

Meaningful Use Stage 2 XML Standards for Clinical Quality Measure Reporting

When implementing an EHR system, or EHR module, that supports the Meaningful Use Stage 2 program for Clinical Quality Measure (CQM) support, there are three HL7 XML standards that you need to be aware of:

• HQMF
• QRDA Category 1
• QRDA Category 3 

The HQMF XML standard defines the data elements referenced by the logic, and associates them to value sets using object identifiers (OIDs).  For those really interested in using the HQMF, you can download the HQMF files for all the Meaningful Use Stage 2 Clinical Quality Measures from the CMS site here.

The QRDA Category 1 XML standard is what is used for expressing patient-level data as inputs to a Clinical Quality Measure calculator, such as popHealth, as part of the Meaningful Use Stage 2 program.  This XML standard allows for EHRs to express the clinical results of individual patients based on the CQM that an EHR system was queried about.  The reports that determine the data to include in the QRDA Category 1 XML are tightly coupled to the HQMF definition of the measure.  I have shared high-level information about the QRDA Category 1 specification, as well as an example of what the QRDA Category 1 XML should look like.

Lastly, the QRDA Category 3 XML standard is what is used to express the summary results of a CQM.  It is unfortunate that the QRDA Category 1 and Category 3 are worded so similarly, yet have significantly different roles in this landscape.  The QRDA Category 3 is the artifact that needs to be generated for expressing summary/aggregate report numbers.  For instance, if the CQM report where created to assess the mammography screening results of women between the ages of 45 and 65 years old, and the result were 73% of that population met that criteria, the QRDA Category 3 XML could express the 73% performance rate, as well as counts associated with the initial patient population, the denominator, the numerator , the exception, and exclusion populations.  You can view an illustration of these different CQM logical families for the proportion-based CQMs here.

Below is an illustration that details the use of these various HL7 XML standards when reporting on Meaningful Use Stage 2 Clinical Quality Measures.

Landscape of Meaningful Use Stage 2 Clinical Quality Measure XML Standards

Hopefully this is helpful.  If not, let me know!

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. © Rob McCready, 2013.

Designing Logos for a Portfolio of Healthcare Projects

Over the past three years, I have been involved with numerous open source healthcare projects, ranging from large reference implementations of Clinical Quality Measures, certification and testing tools adopted by the federal government, a small research project assessing CQM complexity/cost/tractability, this blog, and other ideas that failed to launch.

What I have been endorsing throughout the course of the past three years has been to establish a common "brand" that could be applied to various open source healthcare projects, but still provide a unique look and feel for each individual project.  

The design approach that organically evolved was to develop logos followed the following rules:

• Use only circles
• The circles may come in different sizes and arrangements
• Limiting the logos to a pallet of only two colors
• Color opacity could be changed

You can see the portfolio of these project logos below:

Portfolio of logos for open source healthcare projects I have worked on

Since I am a "left brain" engineer at heart, the various arrangements of the circles has been an easier task than color selection.  I have often struggled with finding the magic combination of colors for anything from these logos to our interior design of our hours.

The best resource that I have found been able to identify is ColorLovers.com.  For those not familiar with the site, it is an evolving resource for palettes and colors that users can share, rank and comment on.

When I had to select to colors, I went to the filter for "most loved" colors of all time, and tried to identify pallets where instead of 5 colors, there were 2 colors used in the pallet.  Hopefully this is a helpful trick.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. © Rob McCready, 2013.