Anesthesiologists perform pre-operative assessment on all patients undergoing surgery in order to identify any disease or risks to the surgery and to plan perioperative anesthetic care to mitigate surgical risk. In patients with known cardiac disease, diagnostic cardiac tests such as stress tests and echocardiograms are sometimes recommended as part of this assessment.
A recommendation from the American Society of Anesthesiologists notes that pre-operative cardiac stress testing is only appropriate for identifying extremely high-risk patients, in whom the results would change management prior to surgery, change the decision of the patient to undergo surgery, or change the type of procedure that the surgeon will perform. 1 The American College of Cardiology and American Heart Association recommends that there is no benefit for routine pre-operative cardiac testing in low risk surgery for patients with no cardiac disease. 2
For this reason, Milliman MedInsight’s Health Waste Calculator identifies pre-operative cardiac testing for patients undergoing low- or moderate-risk non-cardiac procedures (e.g., surgery cataract, laparoscopic cholecystectomy, corneal transplant, and removal of tonsils) as wasteful. We applied the Health Wast Calculator pre-operative testing measure for one of our clients, a commercial health plan with approximately 850,000 members, using claims data from calendar year 2012. As shown in Figure 1, 2.2% of the 5,120 pre-operative cardiac testing services were wasteful, at a cost of $88,000. Hence, it is recommended that clinicians perform pre-operative evaluation appropriately and reduce distress among patients.
|Service Type||Number of Services||Percentage of Services||Aggregate Allowed Amount||Percentage of Allowed Amount
|Total Preoperative Cardiac Testing||5,120||100%||$2,513,987||100%
|Wasteful Preoperative Cardiac Testing||112||2.19%||$88,041||3.50%
It is important to note that claims data alone allows only an approximate identification of wasteful pre-operative cardiac testing. Even so, we were able to confirm instances of pre-operative testing for our client that could help it potentially avoid healthcare costs in the absence of benefit.
For more information on how to identify wasteful services, visit the MedInsight Health Waste Calculator web page.
Identifying and prioritizing patients for targeted interventions is a critically important component of an effective care management program. Direct patient intervention is a relatively costly service and program managers must identify the best candidates for the program in order to maximize clinical outcomes and return on investment for program sponsors.
Analyzing the patient population to better understand overall risks and comorbidities is necessary for projecting the potential impact on cost and utilization that a particular program might achieve. A common approach for assessing and stratifying patients for a disease management program is to use diagnoses and drug codes on claims data to identify patients being treated for targeted conditions and then apply risk adjustment models to determine the relative risk for each patient. Patients with higher risk scores are expected to incur higher costs, so these patients may be targeted first for more intensive interventions. While risk adjusters and relative risk scores can be very useful for care management programs this approach does not take into consideration the complex clinical mix of patients with comorbidities where one condition may be an obstacle to care management interventions targeting another condition. Another approach is to use a more clinically nuanced strategy and stratify patients based on clinical algorithms that do account for the interaction of chronic conditions, such as Milliman’s Chronic Condition Hierarchical Groups (CCHGs).
CCHGs is a patient centric analytic tool consisting of 43 non-overlapping categories. Twenty-four categories are for patients with chronic diseases and 19 categories for health patients (healthy is defined as the absence of a identified chronic condition). The classification rationale is to rank conditions based on how much they influence treatment plans.
The following example illustrates how CCHGs can be used to evaluate the potential of a disease management program designed to reduce admissions for Congestive Heart Failure (CHF). In a demonstration sample of data (refer to Table 1 below) there were 1,571 admissions for CHF (defined by Diagnostic Related Groups). If a program anticipates it can reduce the number of future CHF admissions by 10% (157 admissions), the program managers need to consider the fact that 18% of the CHF admissions in this population were for patients who have renal failure, about 11% of the admissions were for patients with active cancer and about 3.5% were for patients with severe psychiatric conditions. These non-CHF conditions are confounding conditions, meaning they interfere with or even prevent programs targeting CHF being effective. By using the CCHGs we can automatically remove these and other patients with confounding conditions and isolate those CHF patients where we expect to see an impact. In this example, if we subtract the number of admissions for patients with psychiatric conditions, renal failure and active cancer, the total number of target CHF admissions is 1,063. Ten percent of 1,063 equates to 106 admissions.
As health plans and other organizations prepare for Health Reform – including meeting Health Exchange requirements and preparing and competing for the expansion in government program business – there is a renewed focus on meeting accreditation and quality measurement requirements. These requirements are promulgated by organizations such as CMS and state regulators and administered by the National Committee on Quality Assurance (NCQA), URAC and other auditors that are certified by these government agencies.
For experienced health plans with generously staffed Quality Management departments this is old hat to them. To others – newer health plans, provider-sponsored health plans and community-based organizations – the language of the requirements can be foreign and the work required to meet them can seem ominous.
There tends to be theme songs to some of the key quality regulations and requirements. What data do you have that shows your knowledge of the population? How do you use the data to identify opportunities in the population? And how do you measure that your initiatives have had any impact?
Tools like MedInsight, with its clinical analytic and the risk scoring capabilities can be very effective in addressing the needs of some of these requirements. Most organizations are using these tools to identify preventive care gaps and address those gaps but many are missing opportunities to address other critical accreditation and quality improvement areas.
Some of these opportunities include:
- NCQA Health Plan Quality Improvement Standard 7 – requires the ability to identify members with complex illnesses and comorbidities, establish identification methods and conduct an annual population evaluation to determine the continued validity of those methods.
- NCQA Health Plan Quality Improvement Standard 8 – requires the ability to identify condition populations for disease management and implement a stratification approach for selective intervention.
- CMS Special Needs Plan (SNP) Model of Care (MOC)- requires the ability to define the needs of the population, identify frail and high need members and provide interventions based on analysis of population needs.
MedInsight and the Milliman Advanced Risk Adjuster (MARA) provide the ability to efficiently and consistently identify candidates for case and disease management, identify comorbidities and stratify members for targeted intervention. As quality managers and care management leaders gain access to this information these teams will find strong evidence of their ability to meet these accreditation requirements and a solid source of qualified members who will benefit from their programs.