Healthcare Waste: A Conversation with Dr. Mark Fendrick

StickyAnalytics, Evidence Based Decision Making, Evidence-Based Measures, Health Waste Calculator, Value-Based Care, Waste

Dr. Mark Fendrick conceptualized and coined the term Value-Based Insurance Design and is the director of the Value-Based Insurance Design Center (VBID) at the University of Michigan. His research focuses on how clinical payment and consumer engagement initiatives impact access to care, quality of care, and healthcare costs. He has authored over 250 articles and book chapters and has received numerous awards for the creation and implementation of value-based insurance design. He spoke with Milliman MedInsight about healthcare waste and his work with the MedInsight Health Waste Calculator.

Question: You spent your career trying to improve access to high-value care. Why have you gotten into the low-value care arena?

Well, it turns out that most of the services that people consider high value – I like to say the things I beg my patients to do – while they improve individual and population health, they tend to add to overall healthcare costs. Often in modest ways, sometimes in high ways.

So given that most public and private payers are very concerned about the rate of healthcare cost growth, it has been incumbent upon us who have been pushing payers to use more of high-value care to identify potential opportunities to reduce spending on those services that don’t make Americans any healthier to allow us some head room to buy more of those high-value services that are currently underutilized. Early Value-Based Insurance Design, or VBID, just included reductions in consumer out of pocket costs for high-value services. As people used them more often costs went up.

Now we’re trying to identify those low-value services, make them more difficult for people to access, take advantage of those immediate and substantial savings, and hopefully have much of those savings be put back into services that improve Americans’ health.

Question: Where do you think a payer should start when thinking about low-value care?

Low-value care is a little bit tricky in the fact that when you start removing certain services or making it more difficult to access certain services there is going to be special interest and concern, whether it be on the provider side or on the patient side. So we decided to get a number of stakeholders from across the country together and identify services that were easy to find in claims that were almost always low-value when they were used, and that if we got rid of them no one would really mind.

We wanted these to be below the radar, or as I like to say fruit below the ground. This national multi-stakeholder Task Force on Low-Value Care selected five commonly overused services ready for action for purchasers to identify and eliminate. They include: diagnostic testing and imaging prior to low-risk surgery, population based vitamin D screening, prostate specific antigen testing for men over the age of 65, imaging in the first six weeks after muscular-skeleto back pain injury, and the use of branded drugs when a chemically identical generic was available.

It’s our hope that tools like the Health Waste Calculator can help payers identify these and many other services. Services that if they were successfully removed would pretty much not be noticed and would allow payers millions, if not billions, of dollars to spend on those high-value services that are currently out of reach for many people in America.

To read more about our conversation with Dr. Fendrick click here.

Stick to Those Asthma Meds

Sticky, Avoidable Admissions, Drug Utilization, Value-Based Care

Asthma accounted for 3,651 deaths, 2 million emergency department (ED) visits, and about 11 million physician office visits for 2014 in United States (US).[i] The estimated total cost of asthma, including medical expenses, loss of productivity resulting from missed work days, and premature death, was $56 billion in 2007.[ii] Daily long-term control therapy is recommended for patients who have persistent asthma to reduce airway inflammation, control chronic symptoms, and prevent acute asthma attacks.[iii],[iv] Studies have shown that non-adherence to asthma control medications leads to an increase in exacerbations of asthma and subsequent hospitalizations [v],[vi]

To explore the impact of adherence to controller medications on utilization of healthcare services, Milliman conducted an analysis on a large database using MedInsight, Milliman’s healthcare analytics platform. The analysis targeted MARA (Milliman Advanced Risk Adjuster) adjusted utilization of emergency department services related to asthma and any diagnosis (all cause) in members with persistent asthma. The results were compared among three different groups based on adherence to controller medications (adherence below 50 percent, 50-75 percent, and above 75 percent).

Findings:

  1. A statistically significant inverse correlation was found between the adherence rate (proportion of days covered (PDC)) and the number of ED visits.
  2. Asthmatic members enrolled with Medicare having adherence rate above 75 percent had a higher cost utilization as compared to below 50 and 50-75 percent. To some extent this could be explained by the fact that the members with adherence rate above 75 percent had a high comorbidity risk score as indicated by MARA.
  3. Asthmatic members enrolled with Medicaid had the highest overall ED utilization amongst all payer types.

Males generally had a higher ED utilization as compared to females.

Although analysis was based on administrative data, the statistical significance of the results highlights adherence to asthma controller medications as a target for interventions to reduce utilization and improve health outcomes for members with asthma.

[i] Centers for Disease Control and Prevention. Asthma Facts—National Center for Health Statistics, 2014.

Available at: https://www.cdc.gov/nchs/fastats/asthma.htm

[ii] Barnett SBL, Nurmagambetov TA. Costs of asthma in the United States: 2002–2007. J Allergy Clin Immunol 2011; 127(1): 145–52.

Available at: http://www.jacionline.org/article/S0091-6749(10)01634-9/fulltext

[iii] National Asthma Education and Prevention Program Expert Panel Report 3, 2007. Guidelines for the diagnosis and management of asthma.

Available at: http://www.nhlbi.nih.gov/files/docs/guidelines/asthsumm.pdf

[iv] Centers for Disease Control and Prevention. Asthma Stats. Use of long-term control medication among persons with active asthma.

Available at: http://www.cdc.gov/asthma/asthma_stats/longterm_medication.htm

[v] Piecoro LT, et al. Asthma Prevalence, Cost, and Adherence with Expert Guidelines on the Utilization of Health Care Services and Costs in a State Medicaid Population. Health Services Research. 2001; 36(2): 357-71.

Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1089228/pdf/hsresearch00003-0066.pdf

[vi] Stern L, et al. Medication compliance and disease exacerbation in patients with asthma: a retrospective study of managed care data.  Ann Allergy Asthma Immunol.  2006; 97(3): 402-8.

Available at: http://www.sciencedirect.com/science/article/pii/S1081120610608083

Society of Actuaries Podcast: Machine Learning Tools Overview

Stickymachine learning

Machine learning is a hot topic in healthcare analytics today. Shea Parkes and Anders Larson, two of Milliman’s machine learning experts, started a podcast focusing on the subject for the Society of Actuaries (SOA) in 2015. The latest episode was posted May 1, 2018 and is called “Machine Learning Tools Overview.” In the episode, they discuss the range of tools available to help implement machine learning.

“Tools are anything that helps you get the job done. You should do some planning, you should talk some stuff out. But at a certain point, especially with machine learning, someone is going to need to interface with a computer and get something done.” Shea and Anders discuss a range of tools for machine learning, drilling deeper on Graphical User Interface (GUI) modeling tools such as RapidMiner and KNIME. Many of the prior sessions focused on the concepts and theory, only hinting at specific tools and implementations. This episode kicks off a whole new series that will focus on languages, libraries, frameworks, and cloud providers.

Click here to listen to the latest podcast!

Previous episodes include a 12 part series on machine learning from an actuarial perspective. The episodes start with fundamental concepts, then move on to many of the common machine learning algorithms.

Shea Parkes has been in many different roles in his time at Milliman. Now as a Principal he helps lead PRM Analytics with products that focus on Accountable Care and other Organizations. He also participates in many volunteering opportunities with the Society of Actuaries (SOA), mostly within the SOA’s Predictive Analytics and Futurism division. He served on the council of that division for a term and participated in planning many webinars, conference sessions, and newsletter articles.

Anders Larson is a consulting actuary with the Milliman Indianapolis office. He provides actuarial consulting services to commercial and Medicaid health plans, self-funded groups, and provider organizations.

All the episodes of the podcast can be found on the official SOA page here and you can find it on iTunes!

If you would like to learn more about the podcast and or request topics for discussion email Shea Parkes at shea.parkes@milliman.com.

Pharmacy Prescription – Is It Generic Enough?

Sticky, Drug Utilization, Healthcare Analytics

Prescription drugs comprise the third-largest component of U.S. national health expenditures, behind only hospital care and physician services, according to the Centers for Medicare and Medicaid Services.[i] According to the QuintilesIMS Institute, invoiced sales of prescription drugs amounted to $450 billion in 2016, representing 13.4 percent of all U.S. health spending. Prices for prescription drugs in the U.S. are far higher than they are in other industrialized countries. In 2014, on an invoice price basis, the U.S. spent $1,327 per capita on prescription drugs, as compared to non-U.S. members of the Organisation for Economic Co-operation and Development, which had a median per capita drug spending of $489.[ii]

Generic drugs represent an effective and affordable method to decrease pharmacy costs. Generic drugs have saved the U.S. healthcare system $1.67 trillion in the last decade, generating $253 billion in savings in 2016 alone.[iii]

To explore the extent of generic drug usage in pharmacy prescriptions we conducted a focused analysis using drug categories in one of MedInsight’s client databases. Key findings of this analysis were:

  • The proportion of generic prescriptions dispensed has increased consistently over the last 7 years, accounting for 89% of total prescription events in 2016.
  • Generic prescriptions account for only 27% of total prescription costs.
  • Branded drug prescription events decreased by 48% between 2010 to 2016,
  • Branded spending increased by 42% over that same period.

Our observations were based on our analysis of a single customer’s data, however the results are in line with the analysis presented by the Generic Pharmaceutical Association (GPhA) for 2015.[iv]

We further analyzed the prescription events and prescription costs for the top 5 drug categories in terms of total cost in the year 2016. The key observations were:

  • All five categories were found to be directly or indirectly related to a chronic condition.
  • In four out of five categories, the generic prescription rate was greater than 50%.
  • The exception was anti-asthmatic drugs. These were prescribed largely in the form of branded drugs (78%) with a generic prescription rate of only 22% percent.
  • More than 90% of the total prescription costs were attributable to branded drugs in all top five drug categories.

The analysis confirmed market perception and published trend of increasing generic prescription rates despite the bulk of pharmacy expense still driven by the expensive branded drugs.

[i] Centers for Medicare and Medicaid Services. National Health Expenditures 2015 Highlights. Available at: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/highlights.pdf

[ii] Avik Roy,2017.  A Market-Based Plan for Affordable Prescription Drugs. Available at:  https://freopp.org/a-market-based-plan-for-affordable-prescription-drugs-931e31024e08

[iii] Association for Accessible Medicines. 2017 Generic Drug Access and Savings in the U.S. Report. Available at: https://www.accessiblemeds.org/resources/blog/2017-generic-drug-access-and-savings-us-report

[iv] Generic Pharmaceutical Association, 2015. Generic Drug Savings In The U.S. Seventh Annual Edition:2015. Available at: http://www.gphaonline.org/media/wysiwyg/PDF/GPhA_Savings_Report_2015.pdf

 

Clinical Economics of Asthma Controller Medication: Adherence & Outcomes

StickyDrug Utilization, Evidence Based Decision Making, Medical Management

Asthma is a lifelong disease. In addition to limiting a person’s quality of life, medically it is also associated with significant healthcare utilization and costs.[i] According to a survey in 2015, there were 5.9 million physician office visits and 2.0 million Emergency Department visits in the US because of asthma.[ii] [iii] The Centers for Disease Control and Prevention (CDC) estimated that in 2007 asthma alone cost the U.S. about $56 billion in medical costs, lost school and work days, and early deaths.

Good adherence to long-term asthma controller medications, defined as 50% or greater, has been shown to be associated with reduced acute asthma exacerbations in patients with persistent asthma and is one of the key outcome measurements for programs focused on improving persistent asthma management. Despite these facts, actual adherence is only about 30 to 40%.[iv] [v] One likely barrier to achieving higher asthma controller medication adherence is cost. Generic medications, which are usually less expensive than branded medications, were found to have better overall adherence rates.[vi]

To explore asthma controller medication adherence rates, treatment outcomes, and any potential association with branded or generic drugs, Milliman conducted a focused analysis using MedInsight’s healthcare analytic platform on a large database containing claims for over 3.9 million Medicaid, Medicare Advantage and commercial members. We compared the incidence of acute asthma exacerbations and status asthmaticus events among asthma patients (defined as having intermittent or persistent asthma) who were exclusively on branded asthma controller medications to patients who were exclusively on generic asthma controller medications.  We also researched the proportion of generic and branded asthma controller medications and utilization patterns in patients that achieved a high adherence rate. We defined our goal for high adherence as 80% or greater for the proportion of days covered (PDC) with asthma controller medications for patients under treatment for more than three months.

Findings:

  • Asthmatic patients were prescribed 52 types of asthma long-term controller medication drug compositions within the study period. This compares with a total of 69 drug compositions considered to be asthma long-term controller medications (HEDIS AMR-A table).[vii]
  • Forty-two out of the 52 prescribed compositions were branded compositions.
  • Twenty-two percent of asthma patients using generic controller medications exclusively had high adherence rates (80% or higher) compared with 14% of asthma patients who were using branded controller medications exclusively.
  • Asthma patients using generic controller medications exclusively had much lower rates for acute exacerbations and status asthmaticus (34%) than asthma patients using branded controller medications exclusively (62%). Both groups had high adherence rates of over 80%.

  • We also found that patients using generic controller medications exclusively and having adherence rates greater than 80% had 24% lower Emergency Department visits and 32% lower inpatient admissions per thousand member months compared with patients using branded controller medications exclusively.

Our observations were based on limited administrative claims data; however, the results are in line with the analysis presented by association for accessible medicines depicting higher compliance among patients on generic drugs and other studies.vi While potential reasons for this finding such as demographic differences or formulary composition were not investigated, the most likely reason for this observation is differences in asthma severity between the population using generic controller medications and the population using branded controller medications.

References:

[i] American Academy of Allergy, Asthma and Immunology. Allergy and Asthma Drug Guide. Available at: http://www.aaaai.org/conditions-and-treatments/drug-guide.

[ii] Centers for Disease Control and Prevention. National Center for Health Statistics. Last updated: March, 2017. Available at: https://www.cdc.gov/nchs/fastats/asthma.htm.

[iii] National Ambulatory Medical Care Survey: 2015 State and National Summary Tables. Available at:https://www.cdc.gov/nchs/data/ahcd/namcs_summary/2015_namcs_web_tables.pdf

[iv] Asthma Controller Medication Adherence, Risk of Exacerbation, and Use of Rescue Agents Among Texas Medicaid Patients with Persistent Asthma. Published: 2015. Available at: http://www.amcp.org/WorkArea/DownloadAsset.aspx?id=20461.

[v] Mika J. Mäkelä, Vibeke Backer, Morten Hedegaard, Kjell Larsson, Adherence to inhaled therapies, health outcomes and costs in patients with asthma and COPD, Respiratory Medicine, Volume 107, Issue 10, 2013, pages 1481–1490, ISSN 0954-6111. Available at: http://dx.doi.org/10.1016/j.rmed.2013.04.005.

[vi] Generic Drug Access & Savings in the U.S. Association for Accessible Medicines (AAM). 2017 Report. Available at: https://www.accessiblemeds.org/sites/default/files/2017-07/2017-AAM-Access-Savings-Report-2017-web2.pdf.

[vii] HEDIS 2017 Volume 2 NDC Code List. Available at: http://www.ncqa.org/hedis-quality-measurement/hedis-measures/hedis-2017/hedis-2017-ndc-license/hedis-2017-final-ndc-lists.

MedInsight Health Waste Calculator Featured in Health Affairs’ Third Most-Read Article of 2017

StickyHealth Waste Calculator, Healthcare Analytics, Waste

Milliman’s MedInsight Health Waste Calculator – a leading analytic platform that provides actionable data around healthcare quality, efficiency, and effectiveness – was featured in Health Affairs’ third most-read article of 2017! The article, “Low-Cost, High-Volume Health Services Contribute The Most To Unnecessary Health Spending,” highlights the Commonwealth of Virginia’s use of the MedInsight Health Waste Calculator, which found more than $586 million in unnecessary costs in the Virginia All-Payer Claims Database.

Every year the healthcare industry wastes an estimated $750 billion[1]. Milliman created the MedInsight Health Waste Calculator to root out low-value, wasteful services to help significantly reduce healthcare waste for payers, providers, APCDs, and other healthcare systems.

The Virginia Center for Health Innovation (VCHI), Virginia Health Information (VHI), and Value-Based Insurance Design (VBID) Health partnered with MedInsight to support Virginia’s statewide initiatives to identify and reduce low value, unnecessary medical tests and procedures in their healthcare systems. VCHI brought together Virginia’s leading health providers, payers, and employer groups to root out low-value care. Working with the VHI team, they used the MedInsight Health Waste Calculator to aid their measurement, monitoring, and reporting on services that provide no net health benefit.

The Health Affairs article outlines the forty-four low-value health services analyzed in the 2014 data, why they were so costly, and how the MedInsight Health Waste Calculator identified them.

To read full press release or download the full article click here.

 

[1] Institute of Medicine, Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. National Academies Press, 2013.

Overused Ophthalmology Imaging: More May Not Be Better

Sticky, Care Coordination, Episodes of Care, Evidence Based Decision Making, Value-Based Care

Globally, policy makers and payers are focusing on services that are regarded medically unnecessary and do not yield useful results. In the United States, these are referred to as low- or no-value services and are considered wasteful. A significant percentage of the services considered wasteful are medical imaging when done without appropriate indications. Inappropriate use of imaging services contributes to unjustifiable healthcare costs and may lead to irrelevant incidental findings, exposing individuals to unnecessary radiation doses.1

We set out to research wasteful eye imaging services because there is very limited literature or analytical research in this area. According to the American Academy of Ophthalmology Preferred Practice Pattern® guidelines, in the absence of symptoms or significant pathology, a comprehensive history and physical examination should suffice and imaging is not indicated. Advanced eye imaging (optical coherence tomography, visual field testing, fundus photography, or eye photography) should be reserved for malignant neoplasms of the eye, retinal detachments, and injury to eye, optic nerves, or optic chiasm.2

Similar views are advocated by Choosing Wisely® from the American Academy of Ophthalmology, which states: “Don’t routinely order imaging tests for patients without symptoms or signs of significant eye disease in the absence of symptoms or signs of significant disease pathology.”3

We used Milliman MedInsight’s Health Waste Calculator (HWC) to identify wasteful eye imaging services. The HWC ‘Imaging Tests for Eye Disease’ measure identifies eye imaging as wasteful unless the patient has symptoms or signs of significant eye disease such as neoplasms of the eye, choroidal detachment, optic atrophy, glaucoma, diabetic retinopathy, macular degeneration, etc., where imaging is considered medically necessary. We calculated the HWC ‘Imaging Tests for Eye Disease’ measure for one of our clients, a commercial health plan with approximately 2,091,517 members, using claim data from calendar year 2015. As shown in Figure 1, 67% of eye imaging services measured were wasteful and cost nearly $8,198,169.

Figure 1: Pattern of Eye Imaging Services

Service TypeNumber of ServicesPercentage of ServicesAggregate Allowed AmountPercentage of Allowed Amount
Total Eye Imaging157,308100%$12,736,798100%
Necessary Eye Imaging51,88833%$4,538,62934%
Wasteful Eye Imaging105,42067%$8,198,16966%

Figure 2: Percentage Distribution of Wasteful Eye Imaging 

Figure 2 profiles the distribution of the various advance eye imaging modalities that have been identified as wasteful. For each modality, the figure identifies the percentage of services that are wasteful and the corresponding percentage of wasteful total allowed dollars. Optical computed tomography has the highest rate of wasteful services and wasteful costs in the eye imaging service waste category. Analysis highlighted that it was performed for an array of inappropriate diagnoses including but not limited to headache, dizziness, and routine eye exam visits. This supports the proposition that eye imaging is being overused and is often done inappropriately in the absence of significant underlying disease pathology.

Another interesting finding revealed that 41.9% of members with wasteful services had a repeat service for eye imaging in a year, which was also determined wasteful.

It is important to note that claim data alone allows only an approximate identification of wasteful eye imaging. Even so, we were able to confirm a high prevalence of wasteful eye imaging for our client that allowed them to target potentially avoidable utilization and costs.

For more information on how to identify wasteful services, visit the MedInsight Health Waste Calculator web page. [Link to URL: http://www.medinsight.milliman.com/MedInsight/Products/Medinsight-Tools/?prid=71832]

1 Sorenson C, Drummond M, Bhuiyan Khan B. Medical technology as a key driver of rising health expenditure: Disentangling the relationship. ClinicoEconomics and Outcomes Research, 2013:5 223–234.

2 American Academy of Ophthalmology Preferred Practice Patterns Committee. Preferred Practice Pattern Guidelines. Comprehensive Adult Medical Eye Evaluation. Ophthalmology. January 2016. Volume 123, Issue 1, Pages P209–P236.

3      American Association Ophthalmology. Choosing Wisely. Five things physicians and patients should question. February 21, 2013. Accessed at http://www.choosingwisely.org/clinician-lists/american-academy-ophthalmology-routine-imaging-for-patients-without-symptoms-or-signs-of-eye-disease/

Reducing Healthcare Waste – A Three Pronged Approach

Sticky, Waste

Experts and stakeholders across the industry agree that there is an immediate need to reduce wasteful healthcare spending in the US. A 2012 Institute of Medicine report estimated that one third of US healthcare spending, or $750 billion is wasted annually (Washington Post, September 2012). Of that amount, $210 billion is spent on unnecessary services attributable to overtreatment. Overtreatment can result from a number of varying factors. However, regardless of the underlying reasons, the end result is that patients are exposed to treatments and tests that do not result in optimal care and outcomes. In other words, these are healthcare services we shouldn’t be paying for regardless of the cost. More alarming than the huge amounts of money spent on overtreatment, is that many of these tests and treatments and their downstream effects can be harmful to patients.

There are many stakeholders motivated to reduce healthcare waste. Regardless of whether you’re a payer or a provider, the fundamental steps to actually reduce healthcare waste are similar:

  • Identify and quantify wasteful services;
  • Implement strategies to reduce utilization of unnecessary services; and
  • Track and evaluate the success of waste reduction strategies and adjust efforts accordingly.

To identify and quantify wasteful services, Milliman and VBID Health collaboratively developed a tool called the MedInsight Health Waste Calculator. The MedInsight Health Waste Calculator categorizes claims data using logic based on national initiatives such as Choosing Wisely, to quantify services that research has proven add no value in specific clinically nuanced scenarios. These wasteful services are flagged at the claim line level allowing the Health Waste Calculator to quantify utilization and costs associated with wasteful services. With over 400 clinical measures in the development pipeline, the latest version of the MedInsight Health Waste Calculator has 42 measures to quantify waste related to about 60 Choosing Wisely measures from claims data. Quantifying waste allows organizations to prioritize which services to target with waste reduction strategies by aligning internal priorities with metrics on waste prevalence and cost, and factors such as risk of member harm.

There are a number of high profile initiatives and collaborative partnerships aimed at identifying specific wasteful or unnecessary medical services. Two of the most prominent of these collaborative initiatives are Choosing Wisely and the US Preventative Services Task Force (USPSTF). One example of a service that is potentially unnecessary is cardiac imaging in certain situations. Specifically, the American College of Cardiology identified as unnecessary stress cardiac imaging or advanced non-invasive imaging in the initial evaluation of members where there are no cardiac symptoms or no high risk markers present. As noted on the Choosing Wisely website, “asymptomatic, low risk patients account for up to 45 percent of unnecessary screening.” (Choosing Wisely, American College of Cardiology) A more common and familiar category of services that has generated a lot of publicity over the years has been unnecessary prescription of antibiotics for non-bacterial infections. Among the harmful consequences of these services are increased antimicrobial resistance causing severe infections, complications, and longer hospital stays. (Antimicrobial resistance: risk associated with antibiotic overuse and initiatives to reduce the problem)

Once wasteful services have been identified within a population, the obvious question is, what can be done to reduce these unnecessary services. Organizations can leverage several different strategies to address and reduce wasteful services. Strategies are generally centered on one or more levers such as analytics and reporting, education and promotion, claim adjudication, provider network management, medical management, purchasing criteria, and/or benefit design. Strategies will vary based on cost and resources available to implement, and how much they disrupt the healthcare delivery and payment cycle. One of the MedInsight Health Waste Calculator’s earliest clients used the tool’s output to mature their waste reduction strategies and has realized significant savings by reducing wasteful services. By utilizing practice consultants in each of their accountable care organizations and embarking on member awareness campaigns among other interventions, this payer has reduced per member per month costs on ten waste measures ranging from 1.6% savings on unnecessary imaging for uncomplicated rhinosinusitis to 30.2% savings on unnecessary cervical cancer screening in women aged 13 to 20.

Less disruptive strategies, such as member awareness campaigns, will likely have the least amount of impact on reducing wasteful services. Initiating utilization management requirements for potential wasteful services can be costly and disruptive, but would have a high impact on reducing wasteful services. Quantifying unnecessary services in a population can provide guidance on where to focus an organization’s often limited resources. By categorizing identified waste into high volume, low cost services versus high cost, low volume services an organization can better predict expected savings against the cost of various intervention strategies. This article describes analysis of 44 wasteful health services in the Virginia All Payer Claims Database, which revealed that low-cost, high-volume services contributed the most to unnecessary health spending (Mafi, et al., 2017).

Regardless of the strategy, the ultimate objective of reducing wasteful services is to change the behavior of the providers who are ordering the potentially unnecessary services. With this key concept in mind, a group of researchers conducted a study to identify a framework that would be most favorable towards changing physicians’ behavior to reduce low-value care (Parchman, M.L., Healthcare (2016), http://dx.doi.org/10.1016/j.hjdsi.2016.10.005). The researchers conducted interviews with an eight-member stakeholder advisory committee that included patients, providers and health care leaders.  Among their findings, the research group found that providers were most inclined to change their behavior when wasteful services were presented as not only wasteful, but also harmful or potentially harmful to their patients. The overall framework that the researchers found to be most effective in creating the culture for change included:

  • Prioritize addressing low-value care;
  • Build a culture of trust, innovation and improvement;
  • Establish a shared language and purpose; and
  • Commit resources to measurements

Once a strategy has been implemented, the final step is to track and evaluate the effectiveness of the intervention strategy. The same tools and methods used to identify the wasteful or unnecessary services should be used to track these same services over time to determine if there has been an actual reduction in the services that were targeted for intervention. In closing, there is no debate that measurement of waste is critical for any serious approach to reduce unnecessary healthcare services. Integral to a three-pronged approach to reduce wasteful and unnecessary services is identifying, quantifying and then tracking the wasteful services over time.

Wasteful Pre-Operative Cardiac Testing Prior to Low-Risk Surgery

Sticky, Evidence Based Decision Making, Health Waste Calculator, Medical Management, Uncategorized, Value-Based Care

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 TypeNumber of ServicesPercentage of ServicesAggregate Allowed AmountPercentage of Allowed Amount
Total Preoperative Cardiac Testing5,120100%$2,513,987100%
Wasteful Preoperative Cardiac Testing1122.19%$88,0413.50%

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.

Pre-operative Laboratory Testing for Low Risk Surgeries

Sticky, Evidence Based Decision Making, Health Waste Calculator, Population Health, Value-Based Care, Waste

Routinely performing pre-operative testing is a common practice prior to surgery. The American Society of Anesthesiologists (ASA) Task Force defines routine tests as those done in the absence of any specific clinical indication or purpose (i.e. tests intended to discover a disease or disorder in an asymptomatic patient).1   Routine pre-operative testing typically consists of a panel of blood and urine tests, chest X-rays and electrocardiogram.  The purpose is to identify any condition that would add risk to the surgery, and to adjust perioperative anesthetic care to mitigate surgical risk or even postpone or cancel surgery.[1] Although pre-operative testing has long been a common practice there is little or no evidence that supports any benefit for “routine testing” before low risk surgery.[2]

Unnecessary pre-operative testing may cause a patient to be subjected to increased cost of surgical care,   false positive, or borderline results that lead to further follow-up investigations.2 Follow-up investigations due to false positive results can cause unnecessary psychological and economic burdens, postponement of surgery, and even morbidity and mortality.2 In a cost effectiveness study by the Agency for Healthcare Research and Quality (AHRQ), it was found that routine pre-operative investigations resulted in a delay or cancellation of the planned surgery in about 2% of cases, some changes to anesthetic management in up to 11% of cases, or resulted in a change in surgical procedure in under 1% of cases.2

The components of preoperative laboratory testing considered in this blog were metabolic, general health, and electrolyte panel; urinalysis; blood glucose; serum albumin, creatinine, potassium, and sodium; bilirubin; complete blood count (CBC), hemoglobin, bleeding time, prothrombin time; alanine aminotransferase (ALT) and aspartate aminotransferase (AST).

To study the practice pattern of this pre-operative laboratory testing in low risk surgery cases we used the MedInsight Health Waste Calculator’s evidence-based algorithm which includes the Choosing Wisely guidelines for identifying unnecessary testing for low risk surgical procedures. The analysis was conducted on claims from a large Midwestern commercial health plan for the year 2015. Low-risk surgeries considered in this study include various endoscopic and laparoscopic surgeries, minor gynecology, orthopedic, ophthalmology, and urological procedures including superficial surgeries.

We identified 84 percent of pre-operative laboratory testing services associated with low risk surgical procedures as wasteful, meaning that only 16 percent were necessary (Figure 1). The total aggregate allowed cost for pre-operative laboratory testing was $80,445,931, of which wasteful laboratory testing associated with low risk surgical procedures accounted for $1,471,672, which is 2% of the total cost for pre-operative laboratory testing. These results show that wasteful pre-operative laboratory investigations add significant costs for patients and payers.

On further analysis of wasteful laboratory testing services, the top five wasteful pre-operative tests based on total cost were comprehensive metabolic panel, complete CBC, general health panel, urinalysis, hemoglobin (Hb) and prothrombin time (PT). Collectively these five common pre-operative lab tests contributed to 92% of the total wasteful laboratory testing. The profiling of these top five pre-operative tests is shown in Figure 2.

It is important to note that claims data alone is not sufficient to identify wasteful services with absolute certainty. However, even with conservative estimates, these findings confirm that a significant percentage of pre-operative laboratory testing done for low risk surgeries are wasteful.

[1] Apfelbaum JL, Connis RT, Nickinovich DG et al. Practice advisory for preanesthesia evaluation: an updated report by the American Society of Anesthesiologists Task Force on Preanesthesia Evaluation. Anesthesiology. 2012 Mar; 116(3):522–38.

[2] Balk EM, Earley A, Hadar N, Shah N, Trikalinos TA. Benefits and Harms of Routine Preoperative Testing: Comparative Effectiveness. Comparative Effectiveness Review No. 130. (Prepared by Brown Evidence-based Practice Center under Contract No. 290-2012-0012-I.) AHRQ Publication No. 14-EHC009-EF. Rockville, MD: Agency for Healthcare Research and Quality; January 2014

Is Risk Adjustment Enough in Population Analytics?

StickyPopulation Health, Risk Adjustment

Most analysts conducting population health data analysis understand that no two populations are completely the same. Even within a population with similar attributes – people with congestive heart failure, for example – there will be variances in the population subgroups. Many factors contribute to these variances, including age and gender, comorbidities and geographic differences in treatment patterns as well as provider unit pricing and efficiency.

Analysis of cost and utilization data, even with the use of “per member per month” and “per 1,000” calculations, does not produce a fully accurate comparison because of variations in risk or severity of the population. For a more “apples to apples” comparison, a common approach is the application of risk or severity adjustment methodologies to the data to normalize utilization and cost across the populations.

Risk adjustment methodologies, such as the Milliman Advanced Risk Adjuster (MARA), Optum’s Episode Risk Groups, or Verisk’s DxCGs, are common tools used to risk adjust results to account for the variations in populations’ risk and severity.

Consider the example below. Four PCP groups are each treating a population of patients who have Diabetes without Coronary Artery Disease as identified using Milliman’s Chronic Condition Hierarchical Groups. Each medical groups’ population is of varying size and consists of a mix of commercial and Medicare patients. In Figure 1 we see that the total medical and pharmacy Allowed PMPM cost for all services received by the identified patients is highest for Group 3 at $1,063 PMPM. (All services, regardless of diagnosis.)

Figure 1  
Medical Member MonthsAllowed PMPM
Group141,441$828
Group 220,975$893
Group 36,503$1,063
Group 44,869$843
Total73,788$868

Next, using MARA, we take the risk of each population into consideration. As shown in Figure 2, the population attributed to Group 2 has the highest average MARA concurrent risk score at 3.11, and the Group 3 population’s average concurrent risk is slightly lower at 3.06. A higher overall risk score means that the population’s medical conditions are more severe. Adjusting the cost experience for the population severity allows for a more comparable analysis of the cost of the two populations. We see that, even with the risk adjustment, Group 3’s allowed PMPM is well above the other three groups.

Figure 2    
Medical Member MonthsAllowed PMPMAvg MARA Concurrent RiskRisk Adj All PMPM
Group 141,441$8282.97$279
Group 2 20,975$8933.11$287
Group 3 6,503$1,0633.06$348
Group 44,869$8432.89$291
Total73,788$8683.01$288

Now, if we want to compare the efficiency of resource utilization in the treatment of these similar populations, an additional adjustment is needed to separate out unit cost versus resource use. Milliman’s GlobalRVUs are a unit value system that covers the entire range of healthcare services, including physician, hospital, DME, and pharmacy.

In Figure 3 we see that additional calculations have been added to our analysis. The Risk Adjusted RVUs PMPM is the total RVUs (RVUs) for each group’s diabetic population divided by the population’s average MARA concurrent risk score, giving us an overall measure of utilization. Allowed per RVU is the total allowed amount (not adjusted) divided by the total number of RVUs associated with the delivery of services. Utilization Efficiency is calculated as the ratio between the PCP group’s risk adjusted RVUs to the overall average.

Using these data, we see that the main driver of the higher risk-adjusted allowed PMPM is that Group 3’s Utilization Relativity Ratio (based on Risk Adjusted RVU PMPM) is 1.15 or 15% higher than the aggregate for the groups and 19% higher than the next highest group (0.97). We also see that relative price, as measured by Allowed Per RVU, is a relatively small contributor to the difference in risk-adjusted allowed PMPM as the Allowed Per RVU for Group 3 ($45.14) is only 2% higher than the aggregate for the group ($44.39).

Figure 3       
Medical Member MonthsAllowed PMPMAvg MARA Concurrent RiskRisk Adj All PMPMAllowed per RVURisk Adj RVUs PMPMUtilization Relativity Ratio
Group 141,441$8282.97$279$436.460.97
Group 220,975$8933.11$287$466.240.93
Group 36,503$1,0633.06$348$457.701.15
Group 44,869$8432.89$291$466.300.94
Total73,788$8683.01$288$446.681.00

Applying risk adjustment methodologies to cost of care analysis is the approach many analysts use. But is risk adjustment enough? As shown in the example above, leveraging an RVU methodology in conjunction with risk adjustment can provide additional critical insights into variances due to both population severity and provider efficiency.

 

Task Force on Low Value Care – Fall Convening

StickyValue-Based Care

The excitement in the room was palpable as 28 attendees descended on the Westin’s Earhart Room in Detroit, Michigan. Stakeholders from across the nation gathered on September 13 with a common goal—to reduce wasteful healthcare services in the United States. Our charge for the day was to prioritize 29 healthcare services that, in particular clinical scenarios, should not be offered even if they are free. These low-value healthcare services account for an estimated $210 billion or 27% of wasteful healthcare spending in the United States.1 The Task Force on Low Value Care narrowed the list to six measures, meant to serve as a starting point to reduce wasteful care.

Attendees represented a variety of stakeholders. Policy executives from Pfizer, Sanofi, J&J, and Amgen rubbed elbows with large employers such as Caterpillar, Walmart, GE, Comcast, and Hewlett Packard Enterprise. Employer coalitions from the Midwest and Pacific regions joined representatives from the Mid-America Coalition on Healthcare, the National Coalition on Healthcare, Network Strategic Initiatives, and the Independent Colleges and Universities Benefits Association. States were generously represented with attendees from the State Comptroller of Connecticut, the New York City Mayor’s Office of Labor Relations, and the Virginia Center for Health Innovation, which is embarking on the creation of a Virginia Health Value Dashboard. Health plan representation included epidemiology specialists from Kaiser Permanente Washington and Blue Cross Blue Shield of Massachusetts. The National Patient Advocate Foundation brought the patient perspective to the table, and representatives from MedInsight and VBID Health, collaborators on the MedInsight® Health Waste Calculator, provided insight on measuring and acting on the Task Force’s recommendations.

The Task Force used seven key criteria to identify the best services to start with. The first involved considering the potential for patient harm, whether physical, financial, psychological, or due to downstream procedures resulting from over-treatment. Recognizing the need for short-term wins to motivate continued action on this large problem, the Task Force also considered ease of implementation and identified measures where successful examples exist. For example, by requiring providers to select one or more of five evidence-based rationales, a health system in Alberta has seen a 92% reduction in unnecessary vitamin D screening. In order to make a significant impact on the issue, the Task Force also prioritized measures with high unit price, high prevalence, high aggregate spend, and high Waste Index (the proportion of services that are wasteful in each nuanced clinical scenario). Last, but equally as important, the Task Force considered political feasibility, selecting services that affect a variety of conditions in order to minimize consumer and provider backlash.

The list of six selected measures will be released in official communications by the Task Force. Five of the six measures are in production, allowing reliable quantification and trending with the MedInsight Health Waste Calculator.

 

  1. Smith, M., Saunders, R., Stuckhardt, L., & McGinnis, J. (2013). Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC: The National Academies Press.

What a (Low Back) Pain

Sticky, Avoidable Admissions, Avoidable Events, Uncategorized, Value-Based Care

Low back pain is a common problem: more than one in four adults surveyed report having had low back pain during the past three months.[1] Though most low back pain resolves on its own, without medical intervention, it remains one of the most common reasons for physician visits.[2]

Imaging tests of the low back, such as plain X-rays, CT scans, or MRIs, commonly show abnormalities—even in asymptomatic people. Evidence of disk abnormalities, such as degeneration, bulging, protrusion, and annular fissure, is present in over 50% of asymptomatic people in their 30s, and in nearly 90% of people aged 60 or older.[3]

This combination creates a situation where people who undergo imaging tests for low back pain may have abnormalities identified that are unrelated to the pain. Additional tests, consultations, drugs, therapies, and procedures—sometimes even major surgery—may cascade from the initial decision to perform imaging. All this for low back pain that may resolve on its own: what a waste!

Milliman’s MedInsight® Health Waste Calculator can identify wasteful imaging practices for low back pain, using an evidence-based algorithm to analyze claims data in order to classify imaging services as “necessary,” “likely wasteful,” or “wasteful.” For one of our clients, a large commercial health plan, we looked at services performed in 2012. Forty-six percent of services were wasteful; 45% were likely wasteful; and only 9% were necessary. The total aggregate allowed cost for low back pain imaging was $3.3 million, of which wasteful imaging accounted for $2.0 million.

With an evidence-based and conservative approach to measuring the cost of wasteful imaging services—one that does not include the costs of the cascade of additional services—the MedInsight Health Waste Calculator identified an important opportunity to address cost and care for low back pain for our client.

For more information on how the MedInsight Health Waste Calculator can identify wasteful services, click here.

[1] Centers for Disease Control and Prevention (2015). Health, United States, 2015: Table 41: Severe Headache or Migraine, Low Back Pain, and Neck Pain Among Adults Aged 18 and Over, by Selected Characteristics: United States, Selected Years 1997-2014. Retrieved December 2, 2016, from http://www.cdc.gov/nchs/data/hus/2015/041.pdf.

[2] Centers for Disease Control and Prevention. National Ambulatory Medical Care Survey: 2012 State and National Summary Tables: Table 11: Twenty Leading Principal Reasons for Office Visits, by Patient’s Sex: United States, 2012. Retrieved December 2, 2016, from http://www.cdc.gov/nchs/data/ahcd/namcs_summary/2012_namcs_web_tables.pdf.

[3] Brinjikji, W., Luetmer, P.H., Comstock, B., Bresnahan, B.W., Chen, L.E., Deyo, R.A., et al. (April 2015). Systematic literature review of imaging features of spinal degeneration in asymptomatic populations. AJNR Am J Neuroradiol. 2015 Apr;36(4):811-6. doi: 10.3174/ajnr.A4173. Epub 2014 Nov 27. AJNR Am J Neuroradiol. 2015 Apr;36(4):811-6. doi: 10.3174/ajnr.A4173.

Methodology for Identifying Inpatient Admission Events

Sticky, Evidence-Based Measures, Healthcare Analytics

Evidence-based measures (EBMs) and many other health insurance data analytics require counts of inpatient admission events and associated costs. There are multiple ways to identify and count inpatient admission events, each of which may produce a different result. As per the California Department of Health Services, the methodologies used to identify and count individual inpatient admission events vary significantly across different organizations, due to unique data structures and availability.[i] An inpatient event can be identified by various data points like an inpatient Evaluation and Management (E&M) Current Procedure Terminology (CPT) code; an inpatient Uniform Billing (UB) revenue code; or an inpatient bill type. However, when it comes to utilization or counting of inpatient events, the facility claims that contain inpatient Uniform/Universal Billing form (UUB) revenue codes or inpatient bill types are the most credible, and therefore should be the ones used to identify inpatient admissions.

It is important to highlight that an inpatient admission event typically produces more than one claim line, and all of these individual claim lines must be grouped together to constitute the complete inpatient admission event. Various methods are available to do this, with specific advantages and limitations in each method. We have reviewed several of these methods with their potential advantages and shortcomings and have explored the impact of applying these different methods in a representative claims data set. Inpatient claims can come from different types of facilities, including acute hospitals, long-term acute care (LTAC) centers, acute rehabilitation centers, and skilled nursing facilities (SNFs), where there is evidence that the insured stayed overnight.[ii]

Methods

Method 1: All claim lines for a member with same admission date and discharge date constitute a single inpatient admission event when any one of the claim lines have a UB revenue code for inpatient services.

Method 1 accurately identifies an inpatient facility claim line but it fails to account for overlapping dates on claim lines. A single inpatient admission event can have individual claim lines with different admission and discharge dates. This is referred to as “overlapping dates.” Method 1 counts each claim line with a different admission or discharge date as a unique inpatient admission event and calculates the length of stay (LOS) separately for each.

Method 2: Method 1’s logic is used as the first step to identify admission events, followed by a second step grouping all claim lines with overlapping admission and discharge dates into one inpatient admission event.

Method 2 assumes that claim lines with overlapping admission and discharge dates are part of the same continuous stay and counts them as one inpatient admission event.

Method 3: All claim lines for a member with the same admission date and discharge date constitute a single inpatient admission event when any one of the claim lines has either a UB revenue code for inpatient services, or a bill type for inpatient services.

Method 3 casts a wide net to identify inpatient admission events, as it employs more claim types and service codes than Methods 1 or 2. However, similar to Method 1, claim lines with overlapping admissions or discharge dates are not grouped into a single inpatient admissions event. Expanding the data used to identify inpatient admission events increases the probability of having claim lines with overlapping admission or discharge dates.

Method 4: A step to combine claim lines with overlapping admission or discharge dates is added to Method 3.

Method 4 assumes that claim lines with overlapping admission and discharge dates are part of the same continuous stay and counts them as one inpatient admission event.

Method 5: All claim lines for a member with same the claim/encounter ID are grouped into one inpatient admission event when any of the claim lines have a UB revenue code for inpatient services or a bill type for inpatient services.

Method 5 assumes that the claim/encounter identification (ID) remains the same throughout an inpatient admission event, and combines all claim lines with the same claim/encounter ID. This method counts all claim lines with the same claim/encounter ID, even when there is no overlap between admission and discharge dates.

Variation in Results

We applied these different methods for counting inpatient stays on a test database composed primarily of commercial plan members with over 2.6 million lives and 18,551,986 member months for the year 2012 to compare inpatient utilization results for a Healthcare Effectiveness Data and Information Set (HEDIS) EBM: Inpatient Utilization General Hospital/Acute Care (IPU). Results of this comparison are provided in the table in Figure 1.

Figure 1: Comparison of Results Using Different Inpatient Stay Methodologies

Figure 1: Comparison of Results Using Different Inpatient Stay Methodologies

Inpatient admission event counts varied up to 25% across the different methodologies. These results clearly confirm that counts of inpatient admission events can vary significantly depending on the methodology used. Average LOS is also influenced considerably by the method used to identify inpatient admission events.

The outcomes of this analysis emphasize the importance of using a comprehensive methodology when defining inpatient admissions events. It also highlights the need to know the underlying methodology when comparing results for EBMs, for other inpatient admissions event-related measures between organizations, or for different time periods in order to understand if variations in results may be due to the methodology used to define inpatient admissions event rather than operational or quality differences.

[i] California Department of Healthcare Services. Methodology for Identifying Inpatient and Emergency Room Encounters, Appendix A. Retrieved September 7, 2016, from http://www.dhcs.ca.gov/provgovpart/Documents/Appendix%20A.%20Methods.pdf.

[ii] Health Care Cost Institute (September 2012). Health Care Cost and Utilization Report: 2011: Analytic Methodology. Retrieved September 7, 2016, from http://www.healthcostinstitute.org/files/HCCI_HCCUR2011_Methodology.pdf.

Covered California: Leveraging Telemedicine for Network and Quality Management

StickyHealthcare Reform, , ,

Healthcare purchasers, including state-based marketplaces, are making a push to move healthcare payments from volume to value and to transform the delivery system to promote quality of care. Covered California is seeking to do this through its contracting health plans and the qualified health plan (QHP) solicitation process. The most recent solicitation lays out key milestones for the 2017 to 2019 plan years. Beginning in 2017, during the annual QHP certification, a contracting plan will be required to “report on its strategies to ensure that contracting Providers are not charging unduly high prices and for what portion of its entire enrolled population it applies each strategy.” Strategies may include various tactics to move toward value-based payments, drive enrollees to high-value providers, and engage enrollees in decision-making and their own health management.

Milliman MedInsight® can assist health plans and providers with developing appropriate strategies, identifying analytic requirements, and conducting analysis to support them in their network management, quality management, and transformation efforts. We will be releasing a series of articles on these related topics.

To request the first article, “Leveraging Telemedicine and Digital Health Tools as a Strategy for Network and Quality Management,” click here.

Milliman MedInsight Chosen by Two State-Based Organizations and a Leading Community Coalition to Identify Unnecessary Costs and Procedures in Healthcare Delivery

StickyAnalytics, Value-Based Care

Milliman announced today that its healthcare tool, the MedInsight® Health Waste Calculator, has been chosen by two state All Payer Claims Databases (APCDs) and a leading Regional Health Improvement Collaborative to aid in their identification of unnecessary costs and procedures in healthcare delivery.

The Virginia Center for Health Innovation (VCHI), Oregon Health and Hospitals, and the Washington Health Alliance (WHA) have all licensed the MedInsight Health Waste Calculator to support their statewide initiatives to identify and eliminate waste in their healthcare systems.

“In Virginia, an important component of our state health innovation plan is to reduce utilization of low value medical tests and procedures so that we can free up needed resources to advance high value care. Our medical and business communities want to do what they can to better ensure that patients and providers are choosing wisely when it comes to healthcare,” said Beth Bortz, President and CEO of VCHI.

The Health Waste Calculator is an analytic tool that is powered by Milliman’s MedInsight software and encapsulates Value-Based Insurance Design (VBID) Health’s market knowledge on wasteful healthcare spending. The tool identifies and quantifies the use of unnecessary or potentially harmful clinical services, including those defined by national initiatives such as the U.S. Preventive Services Task Force and Choosing Wisely. Using the Health Waste Calculator, each state will be able to report on countless aspects of healthcare but the typical starting points are reports by county, region, or even health system, using these metrics:

• Percentage of individuals exposed to at least one potentially wasteful service.
• Percentage of potentially wasteful services in each market, what is called the Waste and Quality indices.
• Cost metrics of the waste impact as a baseline for them to work from as they implement strategies to realize greater efficiencies.

Mark Fendrick, Director of VBID, stated, “the achievement of Triple Aim in healthcare is to improve quality, enhance patient experience, and lower costs, which requires us to spend more on evidence-based services and less on those that do not produce health. The MedInsight Health Waste Calculator can identify, and potentially reduce, the use of low value services to free up needed resources to reduce important gaps in care.”

MedInsight is used by over 300 health plans, employers, at-risk providers/ACOs, state governments, community health coalitions, and third party administrators.

For more information about Milliman’s MedInsight products, click here.

Potential Overuse of Coronary Angiography Amongst Asymptomatic Population

Sticky, Avoidable Events, Healthcare Analytics

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.

Sources

[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

StickyPCMH, Preventive Care

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,”2 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.

Sources

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

StickyAnalytics, Benchmarking, Healthcare Analytics, Trend Analytics

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.