Potential Overuse of Coronary Angiography Amongst Asymptomatic Population

Coronary angiography is a key diagnostic tool in the management of patients with coronary artery disease. Coronary angiography is used to identify narrowing in coronary arteries and is used for decision support to ascertain the need for revascularization to minimize the risk of myocardial infarction. Various research studies have evaluated trends in the use and results of coronary angiography as key contributory factors to the variations seen in rates for coronary revascularization across the US. For example, a large study by Chan, et al. highlighted that inappropriate revascularization rates ranged from 0% to 55% in different facilities[i]. The use of diagnostic angiography has been advocated in different guidelines to ensure that the revascularization is warranted, however, this approach does not address inappropriate selection of patients for a diagnostic angiography.

According to the American Heart Association (AHA), coronary angiography is not recommended in patients who are at low risk according to clinical criteria and who have not undergone prior non-invasive risk testing. Coronary angiography is also not recommended in asymptomatic patients with no evidence of ischemia or non-invasive testing.[ii]  However, various studies observe that current practice patterns are not consistent with these recommendations. Abdallah, et al. in their review of the National CathPCI registry found that, out of 790,601 elective coronary angiographies performed on patients with no history of Coronary artery disease (CAD), approximately 291,586 angiographies were not preceded by any stress test. Amongst these patients with no stress test prior to coronary angiographies, approximately 38.5% did not have symptoms of angina.[iii]

Other studies have highlighted the variable diagnostic yield of elective diagnostic angiographies. A 2010 Patel et al. study of the cardiovascular national registry found that, in patients without known heart disease who underwent elective invasive angiography, 37.6% had obstructive coronary artery disease.[iv] This implies that nearly two-thirds of patients had a negative result for obstructive coronary artery disease. Amongst the group with no obstructive heart disease, 30.0% were noted to have no symptoms, including no angina.

False positive findings in inappropriate diagnostic tests can trigger subsequent treatment. Referred to as “diagnostic-therapeutic cascade,” inappropriate angiography in asymptomatic patients increases the likelihood of performing inappropriate coronary artery revascularizations and other associated medical utilization. Bradley, et al. studied 544 hospitals that performed more than 1 million elective coronary angiograms and more than 200,000 elective percutaneous coronary interventions (PCI) between 2009 and 2013, and found that 25.1% of patients were asymptomatic at the time of angiography. The proportion of angiographies performed on asymptomatic patients by individual hospitals ranged from 1% to 73.6%. Hospitals with higher rates of angiography performed on asymptomatic patients also had higher rates of inappropriate PCIs and lower rates of appropriate PCIs.v

Considering the volume and cost of these procedures, it is important to reinforce appropriate patient selection for coronary angiography.

To study the practice pattern of coronary angiography and associated services, we reviewed administrative claims data of a Midwestern US Health plan for the July 2011-June 2012 time period. MedInsight’s Health Waste Calculator tool was used to identify necessary, likely wasteful, and wasteful angiography service units. Angiography services for members who had administrative data evidence for cardiac symptoms or high risk diagnosis (ischemic and coronary heart disease, diabetes, and peripherally artery disease) were assigned as necessary services. The analysis in Table 1 shows that 99% of angiography services (n=11,110) met the necessary criteria, while 1% of services were determined to be either likely wasteful or wasteful (n=95), having no evidence for the presence of any the underlying symptoms or diagnosis that would classify these as necessary.

Further analysis highlighted that, amongst the necessary services, 37% were conducted for acute conditions (acute coronary syndrome, angina, and myocardial infarction), while 63% of services were conducted for non-acute conditions. Amongst the inappropriate angiography services, 37% of members received a cardiac stress test prior to the angiography, whereas the remaining 63% of members did not. Due to the absence of clinical data, such as from an Electric Medical Records (EMR) system, we assigned the non-necessary services with a prior stress test as “Likely Wasteful” and the remaining services with no prior stress test as “Wasteful.”

Table 1: Profile of Angiography ServicesProfile of Angiography Services

To study if members with wasteful angiography had other associated costs of follow-up and cardiac revascularization, we reviewed the utilization experience of members with wasteful angiography services for a period of six months after the wasteful service date. Table 2 summarizes the results of this analysis. We restricted our analysis to utilization for cardiac-related procedures and encounters. We found that cardiac revascularizations (including CABG and PCI) and other cardiac procedures comprised 11% of all cardiac related services, with a combined cost of $217,840 (35% of all allowed cardiac related costs). Follow-up diagnostic angiographies and cardiac imaging comprised another 27% of cardiac related services, with a combined cost of $89,290 (14% of all allowed cardiac related costs).

Table 2: Post-Angiography Utilization by Members with Wasteful Services

Post-Angiography Utilization by Members with Wasteful Services

While the results of this analysis are consistent with the low end of what has been reported for inappropriate coronary angiographies (1% to 73.6%)v, it raises concern over the need to improve patient selection for coronary angiography. It is important to note that claims data alone is not sufficient to identify wasteful angiography services with absolute certainty. However, even with conservative estimates, these findings confirm the significant resource use and expenses after angiography and the potential medical waste in the subsequent period.


[i] Chan et al, Appropriateness of Percutaneous Coronary Intervention JAMA. 2011;306(1):53-61. Available at: http://jama.jamanetwork.com/article.aspx?articleid=1104058

[ii] Jeffery L Anderason, Jonathan L. Haperin. “Guideline for the Diagnosis and Management of Patients with Stable Ischemic Heart Disease”; Journal of the American College of Cardiology. 2012; 60(24):2564-2603. Available at http://content.onlinejacc.org/article.aspx?articleid=1391403

[iii] Mouin S. Abdallah, John A. Spertus. “Symptoms and Angiographic Findings of Patients
Undergoing Elective Coronary Angiography Without Prior Stress Testing”; American Journal of Cardiology. Available at http://www.ajconline.org/article/S0002-9149(14)01112-6/pdf

[iv] Manesh R. Patel, Eric D. Peterson. “Low Diagnostic Yield of Elective Coronary Angiography”; New England Journal of Medicine 2010 March 11; 362(10): 886–895. Available at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3920593/pdf/nihms540259.pdf

[v]Bradley SM, Spertus JA. “Patient selection for diagnostic coronary angiography and hospital-level percutaneous coronary intervention appropriateness: insights from the National Cardiovascular Data Registry”; JAMA; 2014;174 (10):1630-1639. Available at http://archinte.jamanetwork.com/article.aspx?articleid=1898877

Supporting Patient-Centered Medical Homes through Healthcare Analytics

Although the concept of Patient-Centered Medical Homes (PCMH) has been around for more than 50 years 1, the last decade has seen a revitalization of the PCMH model and an increase in its presence across the nation. The model’s popularity hinges on an approach to providing comprehensive primary care and redesigning healthcare delivery processes. This is accomplished through an emphasis on team-based care delivery, a whole-person approach to patient care, collaborative relationships between individuals and their physicians, and the use of evidence-based medicine and clinical decision support tools.

In 2007, four nationally recognized physician organizations identified seven principles considered foundational to the PCMH2 model:

  1. Personal physicians
  2. Physician directed medical practices
  3. Whole person orientation
  4. Coordinated/integrated care
  5. Quality and safety
  6. Enhanced access
  7. Payment reform

Although the foundational principles of the PCMH have been largely agreed upon, there is no clear mode for how to create a successful PCMH. One of the most widely recognized models in place today is sponsored by the National Committee for Quality Assurance (NCQA), though there are numerous different demonstration and pilot projects in process across the country. As stated by Stange et al. in their Journal of General Internal Medicine article, “…the context for operationalizing the PCMH is still evolving based on what is being learned in many ongoing demonstrations,”underscoring the importance of evaluating and incorporating unique geographic, demographic, and economic considerations into the design of any new care model.

Successful care delivery transformation projects, especially PCMH implementation and sustainment activities, require significant emphasis on healthcare analytics to inform quality improvement activities in addition to managing cost and utilization control efforts. The use of structured and routine analysis of healthcare claims-based information enables both established organizations and newly developed PCMHs to receive ongoing feedback on process effectiveness and health outcomes, facilitating rapid-cycle process improvement across the organization.

PCMHs typically focus their analytic resources on operational process improvements and patient outcomes, with the goal of driving improvements in support of the Triple Aim. Successful organizations understand that routine and actionable information is the key to driving improvements. Examples of PCMH-focused analytic approaches being used across the country, which typically focus on cost, utilization, and quality, include but are not limited to the following:

  • Increased Use of Generic Pharmaceuticals: Pharmacy claims data is analyzed to identify areas of opportunity for transitioning members to generic equivalents as a cost-reduction initiative.
  • Appropriate Emergency Department (ED) Usage: Utilization patterns in the ED are evaluated to identify high utilization members (and attributed providers) and high frequency conditions. High rates of ED visits may be related to access and availability issues with primary care physicians, underutilization of urgent care services, or lack of understanding that many conditions are more appropriately addressed by a physician office visit rather than an ED visit.
  • Reduced Hospital Admissions and Readmissions: The disease burden of a population is assessed and abnormal utilization rates are identified to assist primary care providers in focusing on specific conditions and/or groups of patients so they can better manage healthcare status and prevent unnecessary hospitalizations and readmissions using outpatient resources.
  • Consistent Practices across Providers: Provider service patterns are compared to identify variations in care practices that could affect all three categories – cost, utilization, and quality. This information can be used to drive adherence to clinical guidelines and identify re-education opportunities for providers who are more expensive than their peers when treating specific cases (e.g., compare costs to treat diabetes patients across providers within a practice to identify discrepancies in care practices and cost savings opportunities). This type of benchmarking analysis can also be used to identify providers who may have high rates of ED usage, or hospital admissions and readmissions within their patient panel.
  • Targeted Care Management Services: Service consumption and diagnosis patterns are analyzed to identify members in need of care management services, for example, members with multiple chronic conditions, mental health issues, or poly-pharmacy usage. Risk scores and clinical drivers can be used to understand individual patient needs and priorities.
  • Preventive Care and Evidence-Based Measures: Claims data is used to assess whether members are consistently receiving preventive care, such as depression screenings, cancer screenings, diabetic eye exams, HbA1c testing, lipid profiles, and blood pressure screenings. Evidence-based care guidelines are implemented for chronic conditions, such as diabetes and cardiovascular disease, and compliance is tracked through claims data.

Experts in the field cite “a growing body of evidence…that the patient-centered medical home is an effective model to transform primary care and serve as a foundation”3 for other accountable care models. Leveraging data analysis is a crucial component of success for organizations pursuing this type of delivery model transformation.


1Deborah Piekes et al. Early Evidence on the Patient-Centered Medical Home. Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services 2012; 12-0020-EF.

2Kurt C Stange et al. Defining and Measuring the Patient-Centered Medical Home. J Gen Intern Med 2010; 25(6):601-12.

3Harbrecht, Marjie G, and Lisa M. Latts. Colorado’s Patient-Centered Medical Home Pilot Met Numerous Obstacles, Yet Saw Results Such as Reduced Hospital Admissions. Health Affairs 2012; 31(9):2010-2017.

Top Five Analytic Trends

It goes without question that the U.S. health insurance industry is in a state of flux.  Americans are buying individual products through health insurance marketplaces, new insurance carriers have entered the market, and Medicaid has been expanded in 29 states and the District of Columbia. These market changes, in addition to other reform provisions already introduced and others just starting to take hold, have subjected the market to an unprecedented level of change.

It is said that insurers like risk but hate uncertainty.  What is for certain today is that the old strategies of accepting good risks and repelling poor risks is no longer a recipe for success.  To thrive in this new environment, health insurers must make smart decisions using data to keep ahead of the competition.

Within that context, here are five areas where Milliman clients are using data and analytics in innovative ways to bring some order to the chaos:

  1. Provider Network Optimization. Despite bending the cost curve, one of the great lessons of the HMO era was that consumers value choice. For years, PPOs competed on network size; employers cared more about network disruption affecting their employees than the cost/volume trade-off. In the face of cost pressures, employers and consumers are now starting to accept that smaller networks may be worth the disruption. To meet this need, plans are deploying sophisticated modeling that combines traditional network access and adequacy measures with reimbursement and quality analytics to develop new “smart” networks.
  1. Value-Based Incentive Programs. It’s widely accepted that fee-for-service reimbursement rewards volume over value. As a replacement for FFS, many payers are promoting value-based incentive strategies that shift reimbursement from fee schedules to bonus pools that pay additional incentives when quality and/ or cost targets are met. Analytics are key to selecting measures, setting thresholds, and assessing provider performance. They also aid providers trying to operate under these new risk arrangements, identifying gaps in care, and benchmarking peer performance.
  1. New Trend Dynamics. While predicting the actual numbers requires the proverbial “crystal ball,” the health insurance industry has a reasonably mature understanding of the drivers of health care cost trend. But things are getting more complicated as physician practice patterns change, populations age but live longer, millions of new consumers flood into the individual and Medicaid markets, and burgeoning innovation (e.g., telemedicine/ telehealth, wearables, smartphones, home visits, retail clinics, etc.) disrupts how and where care is provided. Analytics are key to understanding the “trends in trend” in this new world.
  1. Transparency. The healthcare market has earned a reputation for opaqueness. Consumers are more likely to rely on word-of-mouth when selecting a physician, the price of services depends on who’s paying and has little relationship with the actual cost of services, and information on outcomes and quality is kept locked away from prying eyes. Not so in a post-reform world; consumers can now shop on the basis of price and quality, they can go online and find out how much an appendectomy costs at hospital A or B and which one has a higher success rate, and health plan quality ratings are there for all to see when selecting an exchange plan. Big data and analytics make all of this possible.
  1. Care Management Efficiency. Gone are the days when health insurers had unlimited funding for care management programs. Today, plans must make judicious use of limited administrative dollars to meet medical loss ratio minimums while still managing complex and catastrophic cases.  Analytics help plans optimize their care management programs, prospectively identifying those members most likely to benefit from care management, and then enrolling them in the right program.

With many of their traditional performance management tools neutralized by reform, health insurers have had to get smart about how they leverage data and information: they use analytics to design benefit plans, develop marketing strategies and consumer segmentations, select network providers, develop reimbursement strategies, improve clinical quality, and optimize their remaining cost and quality management tools. In today’s market, how a plan leverages analytics, turning data into actionable information, will make the difference between survival and demise.