MEASURING LOW-VALUE CARE IN MEDICARE

Abstract

Importance

Despite the importance of identifying and reducing wasteful health care utilization, few direct measures of overuse have been developed. Direct measures are appealing because they identify specific services to limit and can characterize low-value care even among the most efficient providers.

Objective

To develop claims-based measures of low-value services, examine service use (and associated spending) detected by these measures in Medicare, and determine if patterns of use are related across different types of low-value services.

Design, Setting and Participants

Drawing from evidence-based lists of services that provide minimal clinical benefit, we developed and trialed 26 claims-based measures of low-value services. Using 2009 claims for 1,360,908 Medicare beneficiaries, we assessed the proportion of beneficiaries receiving these services, mean per-beneficiary service use, and the proportion of total spending devoted to these services. We compared the amount of use and spending detected by versions of these measures with different sensitivity and specificity. We also estimated correlations between use of different services within geographic areas, adjusting for beneficiaries’ sociodemographic and clinical characteristics.

Main Outcome Measures

Use and spending detected by 26 measures of low-value services in 6 categories: low-value cancer screening; low-value diagnostic and preventive testing; low-value preoperative testing; low-value imaging; low-value cardiovascular testing and procedures; and other low-value surgical procedures.

Results

Services detected by more sensitive versions of measures affected 41% of beneficiaries and constituted 2.7% of overall annual spending. Services detected by more specific versions of measures affected 24% of beneficiaries and constituted 0.6% of overall spending. In adjusted analyses, low-value spending detected in geographic regions at the 5^th percentile of the regional distribution of low-value spending ($221/beneficiary) exceeded the difference in detected low-value spending between regions at the 5^th and 95^th percentiles ($186/beneficiary). Adjusted regional use was positively correlated among 5 of 6 categories of low-value services (r for pair-wise, between-category correlations ranged 0.14–0.56, mean 0.35; P≤0.01).

Conclusions and Relevance

Services detected by a limited number of measures of low-value care constituted modest proportions of overall spending but affected substantial proportions of beneficiaries and may be reflective of overuse more broadly. Performance of claims-based measures in supporting targeted payment or coverage policies to reduce overuse may depend heavily on measure definition.

Several recent initiatives, including the “Choosing Wisely” campaign by the American Board of Internal Medicine Foundation,¹ have focused on directly defining wasteful health care services that provide little or no health benefit to patients. It is challenging, however, to translate evidence-based lists of low-value services generated by such initiatives into meaningful metrics that can be applied to available data sources such as insurance claims.² The value of most services depends on the clinical situation in which they are provided, and administrative data often lack the clinical detail necessary to distinguish appropriate from inappropriate use. Consequently, the number of low-value services that can be reliably identified in claims data may be limited, and the amount of low-value care detected by claims-based measures may be highly sensitive to how the measures are defined.

Direct approaches to measuring overuse may nevertheless be useful for characterizing the potential extent of wasteful care and informing policies to address low-value practices. Indirect approaches to measuring care efficiency, such as comparing total risk-adjusted spending per patient across geographic areas or provider organizations,³ may be challenging for policymakers and providers to act upon because specific services contributing to wasteful spending are not identified.⁴ Furthermore, such relative measures may fail to characterize the full extent of low-value practices if they are widespread. In contrast, direct measures could be used to identify specific instances of overuse and assess their frequency among even the most efficient providers. In addition, even a limited set of direct measures could be useful for monitoring low-value care if it reflects underlying drivers of overuse more broadly. For analogous reasons, many quality measures relating to underuse have been developed and applied widely in quality-improvement initiatives despite similar measurement challenges.^5,6

Drawing from evidence-based lists and the medical literature, we created algorithms to measure selected low-value services that could be applied to insurance claims data with reasonable accuracy despite the limited clinical information in claims. Using 2009 Medicare claims, we examined the use of these services and their associated spending, varying the sensitivity and specificity with which the measures likely identified overuse. We also examined whether use of different types of low-value care was correlated within regions; positive correlations might suggest that the measures reflect common drivers of overuse.

Methods

Data Sources and Sample Population

We analyzed 2008–2009 claims data for a random 5% sample of Medicare beneficiaries, as well as demographic information from enrollment files and chronic conditions from the Chronic Condition Data Warehouse (CCW).⁷ We applied measures of low-value services to 2009 claims, using 2008 claims and the CCW for relevant clinical history. Our study population consisted of 1,360,908 beneficiaries who were continuously enrolled in Part A and B of traditional fee-for-service Medicare in 2008 and while alive in 2009. We further restricted the study population to individuals who, in 2009, were living in US states or Washington DC and were age 65 or older. Our study was approved by the Harvard Medical School Committee on Human Studies and the Privacy Board of the Centers for Medicare and Medicaid Services.

Measures of Low-Value Services

We considered services that have been characterized as low-value by the American Board of Internal Medicine Foundation’s Choosing Wisely initiative,⁸ the US Preventive Services Task Force “D” recommendations,⁹ the National Institute for Health and Care Excellence “do not do” recommendations,¹⁰ the Canadian Agency for Drugs and Technologies in Health health technology assessments,¹¹ or peer-reviewed medical literature.¹² These services have been found to provide little to no clinical benefit on average, either in general or in specific clinical scenarios. From these services, we selected a subset that is relevant to the Medicare population and could be detected using Medicare claims with reasonable specificity, meaning that major clinical factors distinguishing likely overuse from appropriate use could be identified or approximated with claims and enrollment data (eAppendix). We also required the evidence base characterizing each service as low-value to have been established prior to 2009. Many low-value services were not selected (e.g., imaging for pulmonary embolism without moderate or high pre-test probability⁸) because of difficulty distinguishing inappropriate from appropriate use with claims data.

For each selected service, we developed an operational definition of low-value occurrences using Current Procedural Terminology (CPT) procedure codes, Berenson-Eggers Type of Service (BETOS) codes, International Classification of Diseases (ICD-9) diagnostic codes, CCW condition indicators, timing of care, site of care, and demographic information (eTable1). When supported by clinical evidence or guidelines, we broadened the scope of some recommendations featured in lists of low-value services. For example, we expanded the Choosing Wisely definition of low-value preoperative pulmonary testing before cardiac surgery to include pre-operative pulmonary testing before low to intermediate-risk surgeries more broadly.¹³ We also combined similar low-value services (e.g. various laboratory tests for hypercoaguable states) into single measures. Table 1 presents the operational definitions for the 26 measures of low-value care we developed and applied to claims.

Table 1.

Measures of low-value services

			Operational Definition
	Measure	Source and supporting literature	More sensitive, less specific Base definition	Less sensitive, more specific Additional restrictions
Cancer Screening	Cancer screening for patients with chronic kidney disease (CKD) receiving dialysis	CW17	Screening for cancer of the breast, cervix, colon, or prostate for patients with chronic kidney disease receiving dialysis services	Only patients over age 75a
	Cervical cancer screening for women over age 65	CW, USPSTF18	Screening Papanicolaou test for women over age 65	No personal history of cervical cancer or dysplasia noted in claim or in prior claimsb No diagnoses of other female genital cancers, abnormal Papanicolaou findings, or human papillomavirus positivity in prior claimsb
	Colorectal cancer screening for older elderly patients	USPSTF19	Colorectal cancer screening (colonoscopy, sigmoidoscopy, barium enema, or fecal occult blood testing) for patients over age 75	No history of colon cancer Only screening (i.e. not diagnostic) procedure codes Only patients over age 85
	Prostate-specific antigen (PSA) testing for men over age 75	USPSTF20	PSA test for patients over age 75	No history of prostate cancer Only screening (i.e. not diagnostic) procedure codes
Diagnostic and Preventive Testing	Bone mineral density testing at frequent intervals	Literature21,22	Bone mineral density test less than two years after a prior bone mineral density test	Only patients with a diagnosis of osteoporosis prior to the initial bone mineral density testc
	Homocysteine testing for cardiovascular disease	Literature23	Homocysteine testing	No diagnoses of folate or B12 deficiencies in claim and no folate or B12 testing in prior claimsb
	Hypercoagulability testing for patients with deep vein thrombosis	CW24	Lab tests for hypercoagulable states within 30 days following diagnosis of lower extremity deep vein thrombosis or pulmonary embolism	No evidence of recurrent thrombosis, defined by diagnosis of deep vein thrombosis or pulmonary embolism more than 90 days prior to claim
	Parathyroid hormone (PTH) measurement for patients with stage 1–3 CKD	NICE25,26	PTH measurement in patients with chronic kidney disease	No dialysis services before PTH testing or within 30 days following testing No hypercalcemia diagnosis in any 2009 claim
Preoperative Testing	Preoperative chest radiography	CADTH CW27,28	Chest x-ray specified as a preoperative assessment or occurring within 30 days prior to a low or intermediate risk non-cardiothoracic surgical proceduree	No x-rays related to inpatient or emergency cared Only x-rays that preceded a low or intermediate risk non-cardiothoracic surgical procedure (i.e. excluding x-rays specified as preoperative before other procedures)e
	Preoperative echocardiography	CW29	Echocardiogram specified as a preoperative assessment or occurring within 30 days prior to a low or intermediate risk non-cardiothoracic surgical proceduree	No echocardiograms related to inpatient or emergency cared Only echocardiograms that preceded a low or intermediate risk non-cardiothoracic surgical proceduree
	Preoperative pulmonary function testing (PFT)	CW13	PFT specified as a preoperative assessment or occurring within 30 days prior to a low or intermediate risk surgical proceduref	No PFTs related to inpatient or emergency cared Only PFTs that preceded a low or intermediate risk surgical proceduref
	Preoperative stress testing	CW30	Stress electrocardiogram, echocardiogram, or nuclear medicine imaging specified as a preoperative assessment or occurring within 30 days prior to a low or intermediate risk non- cardiothoracic surgical proceduree	No stress testing related to inpatient or emergency cared Only stress testing that preceded a low or intermediate risk non-cardiothoracic surgical proceduree
Imaging	Computed tomography (CT) of the sinuses for uncomplicated acute rhinosinusitis	CW31	Maxillofacial CT study with a diagnosis of sinusitis in the imaging claim	No complications of sinusitis,g immune deficiencies, nasal polyps, or head/face trauma noted in claim No chronic sinusitis patients, defined by sinusitis diagnosis between 1 year and 30 days prior to imaging
	Head imaging in the evaluation of syncope	CW NICE32	CT or Magnetic Resonance Imaging (MRI) of the head with a diagnosis of syncope in the imaging claim	No diagnoses in claim warranting imagingh
	Head imaging for uncomplicated headache	CW33	Head CT/MRI with diagnosis of (non-thunderclap, non-post-traumatic) headache	No diagnoses in claim warranting imagingi
	Electroencephalogram for headaches	CW34	EEG with headache diagnosis in the claim	No epilepsy or convulsions noted in current or prior claimsb
	Back imaging for patients with non- specific low back pain	CW, NICE35	Back imaging with a diagnosis of lower back pain	No diagnoses in claim warranting imagingj Imaging occurred within six weeks of the first diagnosis of back pain
	Screening for carotid artery disease in asymptomatic adults	CW, USPSTF36	Carotid imaging for patients without a history of stroke or transient ischemic attack (TIA) and without a diagnosis of stroke, TIA, or focal neurological symptoms in claim	Test not associated with inpatient or emergency carek
	Screening for carotid artery disease for syncope	CW32	Carotid imaging with syncope diagnosis	No history of stroke or TIA No stroke, TIA, or focal neurological symptoms noted in claim
Cardiovascular testing and procedures	Stress testing for stable coronary disease	CW37 Literature38	Stress testing for patients with an established diagnosis of ischemic heart disease or angina (at least 6 months prior to the stress test) and thus not done for screening purposes	Test not associated with inpatient or emergency care, which might be indicative of unstable anginak Only patients with a past diagnosis of myocardial infarction in order to exclude patients with a history of non-cardiac chest pain in accurately coded as angina (i.e., those with no underlying ischemic heart disease who might benefit from screening and optimization of medical management)
	Percutaneous coronary intervention with balloon angioplasty or stent placement for stable coronary disease	Literature38,39	Coronary stent placement or balloon angioplasty for patients with an established diagnosis of ischemic heart disease or angina (at least 6 months prior to the procedure) Procedure not associated with an ER visit,kwhich might be indicative of acute coronary syndrome	Only patients with a past diagnosis of myocardial infarction in order to exclude patients with a history of non-cardiac chest pain inaccurately coded as angina
	Renal artery angioplasty or stenting	Literature40,41	Renal/visceral angioplasty or stent placement	Diagnosis of renal atherosclerosis or renovascular hypertension noted in procedure claim
	Carotid endarterectomy in asymptomatic patients	CW36,42	Carotid endarterectomy for patients without a history of stroke or TIA and without stroke, TIA, or focal neurological symptoms noted in claim	Operation not associated with an ER visitk Only female patientsl
	Inferior vena cava filters for the prevention of pulmonary embolism	Literature43,44	Any IVC filter placement
Other surgery	Vertebroplasty or kyphoplasty for osteoporotic vertebral fractures	Literature45–48	Vertebroplasty/kyphoplasty for vertebral fracture	No bone cancers, myeloma, or hemangioma noted in procedure claim
	Arthroscopic surgery for knee osteoarthritis	NICE49,50	Arthroscopic debridement/chondroplasty of the knee with diagnosis of osteoarthritis or chondromalacia in the procedure claim	No meniscal tear noted in procedure claim

CW = Choosing Wisely; USPSTF = U.S. Preventive Services Task Force C or D recommendations; NICE = National Institute for Health and Care Excellence “do not do” list; CADTH = Canadian Agency for Drugs and Technologies in Health health technology assessments.

^aThis age cutoff is included because the distribution of kidney transplant ages within the sample suggests transplantation is uncommon over age 75.

^bPrior claims refer to all claims from 01/01/2008 until one day prior to the service of interest.

^cThis restriction limits the measure to testing of patients with osteoporosis.

^dInpatient-associated is defined as occurring during within 30 days following an inpatient stay. ER-associated is defined as occurring during or one day after an ER visit.

^eProcedures include surgeries of the breast, colectomy, cholecystectomy, transurethral resection of the prostate, hysterectomy, orthopedic surgeries besides hip and knee replacement, corneal transplant, cataract removal, retinal detachment, hernia repair, lithotripsy, arthroscopy, and cholecystectomy. 30-day window between preoperative testing and surgery was derived empirically based on distribution of intervals between test and procedure.

^fProcedures include surgeries listed immediately above as well as coronary artery bypass graft, aneurysm repair, thromboendarterectomy, percutaneous transluminal coronary angioplasty, and pacemaker insertion.

^gComplications of sinusitis include eyelid inflammation, acute inflammation of orbit, orbital cellulitis, or visual problems.

^hExclusion diagnoses include epilepsy, giant cell arteritis, head trauma, convulsions, altered mental status, nervous system symptoms (e.g. hemiplegia), disturbances of skin sensation, speech problems, stroke, transient ischemic attack, history of stroke.

ⁱExclusion diagnoses include those listed in vii as well as cancer and history of cancer.

^jExclusion diagnoses include cancer, trauma, intravenous drug abuse, neurological impairment, endocarditis, septicemia, tuberculosis, osteomyelitis, fever, weight loss, loss of appetite, night sweats, and anemia.

^kInpatient-associated is defined as occurring during an inpatient stay. ER-associated is defined as occurring during or within 14 days after an ER visit.

^lRestriction is based on sex-specific subgroup analyses of procedure efficacy in the referenced literature.

Inherent in most of our claims-based measures of low-value care was a trade-off between sensitivity (greater capture of inappropriate use) and specificity (less misclassification of appropriate use as inappropriate). To assess the variability of our findings across a spectrum of these important measurement properties, we specified two versions of each measure, one with higher sensitivity (and lower specificity) and the other with higher specificity (and lower sensitivity) for detecting low-value care (Table 1). Even without a gold standard for assessing service appropriateness, the relative sensitivity and specificity of our measures can be inferred from the clinical criteria we applied. For example, limiting the colorectal cancer screening measure to beneficiaries over age 85 instead of 75 decreases its sensitivity (fewer low-value instances detected) but increases its specificity (smaller proportion of appropriate services misclassified as inappropriate).

We calculated spending on low-value services using standardized prices to adjust for regional differences in Medicare payments. We used the median spending per service nationally as the standardized price for each service, including payments from Medicare, beneficiary coinsurance amounts, and any payments from other primary payers. We included related services typically bundled with the low-value service in these price estimates (e.g. contrast administration for an imaging study or anesthesia for a procedure). These bundles were defined based on examination of the most frequent CPT codes appearing during the day a low-value service was provided and thus would not include subsequent care prompted by the service (e.g., further imaging for incidental findings on pre-operative chest x-rays). Additional information on service detection and pricing, including the specific codes (CPT, BETOS, etc.) employed, is available in the eAppendix.

Statistical Analyses

We counted the number of times each beneficiary experienced each low-value service and calculated the per-beneficiary spending for each service. From these values, we calculated the percentage of beneficiaries receiving at least 1 low-value service and the aggregate spending for all beneficiaries for each service and in each of 6 service categories: low-value cancer screening; low-value diagnostic and preventive testing; low-value preoperative testing; low-value imaging; low-value cardiovascular testing and procedures; and other low-value surgical procedures. Aggregate spending estimates were multiplied by 20 to approximate spending for the entire Medicare population from 5% samples. We also calculated the proportion of total spending for services covered by Part A and B of Medicare (including coinsurance amounts and payments from other primary payers) devoted to services detected by low-value care measures.

We used hospital referral regions (HRRs) to examine how utilization of different types of low-value services was related among the same groupings of providers. Although we were not interested in geographic areas per se and although practices patterns vary both within and between areas,⁴ HRRs nevertheless served as a useful unit of comparison to determine if groups of providers that were more likely to provide one type of low-value service were more likely to provide another. First, we estimated mean per-beneficiary utilization counts in each service category at the HRR level using linear regression models with HRR fixed effects. To control for beneficiaries’ sociodemographic and clinical characteristics, we included as covariates age, age squared, sex, race, indicators of 21 CCW diagnoses present before 2009 (derived from claims dating back to 1999), indicators of having multiple comorbid conditions (2 to 7+), the Rural-Urban Continuum Code for beneficiaries’ county of residence, and several socioeconomic measures of the elderly population at the zip code tabulation area level (median income, percent below the federal poverty level, and percent with a high school degree). To account for additional dimensions of case mix not captured by the CCW, we included indicators of conditions that qualified patients for potential receipt of several low-value services (e.g., a diagnosis of headache in 2009 qualifying beneficiaries for potentially inappropriate head imaging; see eAppendix for details). For each pair of low-value service categories, we then estimated correlations between regional means in adjusted utilization, weighted by the number of traditional fee-for-service Medicare beneficiaries in each HRR. Correlations were not substantially altered by use of random effects to estimate regional means or by the addition of indicators of qualifying conditions.

Results

Among 1,360,908 beneficiaries in the study sample, 1,050,775 instances of care provision (77 services per 100 beneficiaries) were detected by the more sensitive measures of low-value services, corresponding to 21.0 million instances for the entire traditional Medicare population in 2009. Forty-one percent of beneficiaries received at least 1 service detected by the more sensitive measures. Our more specific but less sensitive measures of low-value care detected 424,207 services (31 per 100 beneficiaries), corresponding to 8.5 million services for the entire Medicare population. Twenty-four percent of beneficiaries received at least 1 of these services.

Spending for services detected by our more sensitive measures of low-value care totaled $8.2 billion for the entire Medicare population, or $303 per beneficiary, while spending for services detected by our more specific measures totaled $1.8 billion, or $66 per beneficiary. These amounts comprised 2.7% and 0.6%, respectively, of total annual spending in 2009 on services covered by Part A and B of Medicare.

Figure 1 presents utilization rates and their associated spending, decomposed by category of low-value care measures. Imaging, cancer screening, and diagnostic and preventive testing measures detected most of the utilization, whereas measures of imaging and cardiovascular testing and procedures detected most of the spending (see eTable 2 for these results in tabular form). Table 2 presents utilization rates and associated spending captured by each of the 26 measures of low-value care. Individual measures with major contributions to spending included both high-price, low-utilization items such as percutaneous coronary intervention for stable coronary disease and low-price, high-utilization items such as screening for asymptomatic carotid artery disease.

Figure 1. Utilization rates and associated spending for services detected by low-value care measures among Medicare beneficiaries in 2009.

Count refers to the number of unique incidences of service provision. Overall spending refers to total spending on all services covered by Part A and B of Medicare. See Table 1 for services included in each category and for operational definitions of all measures.

Table 2.

Service counts and associated spending detected by measures of low-value care

	More sensitive version of measures						More specific version of measures
Measure (abbreviated)	count (per 100 benefi- ciaries)a	% of low- value count	% of benefi- ciaries affected	spending (millions)	% of low- value spending	% of overall spendingb	count (per 100 benefi- ciaries)a	% of low- value count	% of benefi- ciaries affected	spending (millions)	% of low- value spending	% of overall spendingb
Imaging for non-specific low back pain	12.4	16%	9.4%	226	3%	0.07%	4.5	14%	4.1%	82	5%	0.03%
PSA screening over age 75	12.0	16%	8.3%	98	1%	0.03%	2.8	9%	2.7%	23	1%	0.01%
PTH testing in early CKD	7.9	10%	2.5%	137	2%	0.04%	3.1	10%	1.7%	53	3%	0.02%
Stress testing for stable coronary disease	7.8	10%	7.3%	2,065	25%	0.67%	0.8	3%	0.8%	212	12%	0.07%
Colon cancer screening for older elderly patients	7.7	10%	6.9%	573	7%	0.18%	0.9	3%	0.8%	7	0%	0.00%
Cervical cancer screening over age 65	7.0	9%	6.9%	120	1%	0.04%	6.5	21%	6.4%	111	6%	0.04%
Carotid artery disease screening for asymptomatic patients	6.6	9%	6.0%	323	4%	0.10%	5.6	18%	5.1%	274	15%	0.09%
Preoperative x-ray	5.5	7%	5.1%	75	1%	0.02%	1.6	5%	1.6%	22	1%	0.01%
Homocysteine testing for cardiovascular disease	2.0	3%	1.5%	15	0%	0.00%	0.8	3%	0.6%	6	0%	0.00%
Head imaging for syncope	1.5	2%	1.4%	87	1%	0.03%	1.1	3%	1.0%	61	3%	0.02%
Bone mineral density testing at frequent intervals	1.0	1%	1.0%	20	0%	0.01%	0.8	3%	0.8%	17	1%	0.01%
Carotid artery disease screening for syncope	1.0	1%	1.0%	49	1%	0.02%	0.7	2%	0.7%	33	2%	0.01%
PCI/stenting for stable coronary disease	0.8	1%	0.7%	2,810	34%	0.91%	0.1	0%	0.1%	212	12%	0.07%
Preoperative echocardiography	0.8	1%	0.8%	58	1%	0.02%	0.3	1%	0.3%	21	1%	0.01%
Preoperative stress testing	0.7	1%	0.7%	180	2%	0.06%	0.3	1%	0.3%	81	4%	0.03%
CT scan for rhinosinusitis	0.6	1%	0.6%	42	1%	0.01%	0.3	1%	0.3%	23	1%	0.01%
Renal artery stenting	0.4	0%	0.3%	705	9%	0.23%	0.1	0%	0.1%	139	8%	0.04%
Vertebroplasty	0.3	0%	0.3%	199	2%	0.06%	0.3	1%	0.3%	196	11%	0.06%
Arthroscopic surgery for knee osteoarthritis	0.2	0%	0.2%	143	2%	0.05%	0.1	0%	0.1%	63	4%	0.02%
Cancer screening for CKD patients on dialysis	0.2	0%	0.2%	4	0%	0.00%	0.1	0%	0.1%	1	0%	0.00%
IVC filter placement	0.2	0%	0.2%	43	1%	0.01%	0.2	1%	0.2%	43	2%	0.01%
Preoperative PFT	0.2	0%	0.2%	2	0%	0.00%	0.1	0%	0.1%	1	0%	0.00%
Head imaging for headache	0.1	0%	0.1%	8	0%	0.00%	0.1	0%	0.1%	5	0%	0.00%
Carotid endarterectomy for asymptomatic patients	0.1	0%	0.1%	263	3%	0.08%	0.1	0%	0.0%	110	6%	0.04%
Hypercoagulability testing after DVT	0.1	0%	0.1%	3	0%	0.00%	0.0	0%	0.0%	1	0%	0.00%
EEG for headache	0.0	0%	0.0%	1	0%	0.00%	0.0	0%	0.0%	0	0%	0.00%
Total	71	100%	41%c	8,248	100%	2.7%	31.2	1	24%c	1,798	100%	0.6%

CKD = chronic kidney disease;CT = computed tomography; DVT = deep vein thrombosis; EEG = Electroencephalogram; IVC = inferior vena cava; PCI = Percutaneous coronary intervention; PFT = pulmonary function testing; PSA = prostate-specific antigen;PTH = parathyroid hormone.

^aCount refers to the number of unique incidences of service provision.

^bOverall spending refers to annualspending for services covered by Part A and B of Medicare. See Table 1 for service category assignments and for operational definitions of all measures.

^cTotal does not equal column sum because some patients received multiple different services.

Table 3 presents correlations between adjusted levels of regional service use in different categories of low-value care, as detected by our more sensitive measures. Per-beneficiary utilization counts were positively correlated with one another for 5 of the 6 categories. Correlation coefficients ranged from 0.14 to 0.56 across all pair-wise combinations of these 5 categories (P≤0.01), with a mean of 0.35. Non-cardiovascular surgical procedures were not positively correlated with utilization in other categories of measures. The measures exhibited good internal consistency across all categories (Chronbach’s alpha, 0.69).

Table 3.

Pearson’s correlation coefficients relating regional use between different categories of measures of low-value care

Category	Cancer screening	Diagnostic and preventive testing	Preoperative testing	Imaging	Cardiovascular testing and procedures	Other surgery
Cancer screening	1	-	-	-	-	-
Diagnostic and preventive testing	0.34**	1	-	-	-	-
Preoperative testing	0.31**	0.14*	1	-	-	-
Imaging	0.56**	0.38**	0.33**	1	-	-
Cardiovascular testing and procedures	0.29**	0.29**	0.27**	0.55**	1	-
Other surgery	−0.13*	−0.07	−0.16**	−0.03	0.05	1

^*p-value <0.05

^**p-value <0.01

Adjusted regional spending on services detected by more sensitive measures of low-value care ranged from $221 per-beneficiary in the 5^th percentile to $407 per-beneficiary in the 95^th percentile of HRRs (median, $297; inter-quartile range, $264 to $336). Thus, low-value spending detected in regions at the 5^th percentile of the regional distribution exceeded the difference in detected low-value spending between regions at the 5^th and 95^th percentiles ($186/beneficiary).

Comment

In this national study of selected low-value services, Medicare beneficiaries commonly received care that was likely to provide minimal or no benefit on average. Even when applying narrower versions of our limited number of measures of overuse, we identified low-value care affecting roughly one quarter of Medicare beneficiaries. These findings are consistent with the notion that wasteful practices are pervasive in the US health care system.

Within regions, different types of low-value utilization generally exhibited significantly positive correlations with one another, ranging from weak to moderate in strength, although one category of low-value utilization (non-cardiovascular surgical procedures) was not positively correlated with the others. These findings suggest that many, but not all, low-value services may be driven by common factors. Therefore, claims-based measures, although limited in number and the amount of wasteful spending they detect, could be useful for monitoring low-value care more broadly, including some care that may be difficult to measure with claims.

While these findings suggest that direct approaches to measuring wasteful care may be tractable and informative, other findings underscore potential challenges in developing and applying direct measures of overuse. In particular, the amount of low-value care we detected varied substantially with the clinical specificity of our measures. Estimates of the proportion of Medicare beneficiaries receiving one or more measured low-value service decreased from 41% to 24%, and the contribution of low-value spending to total spending decreased from 2.7% to 0.6%, when we employed more restrictive definitions that traded off sensitivity for specificity. For example, our more sensitive measure of low-value imaging for low back pain captured more inappropriate use of imaging studies at the expense of including some appropriate use. Our more specific measure was less likely to include appropriate use but probably excluded many low-value studies, as suggested by the 3-fold reduction in the number of studies captured.

Thus, the performance of administrative rules to reduce overuse through coverage policy, cost-sharing, or value-based payment (e.g., pay for performance) may depend heavily on measure definition. Such strategies may be appropriate for select services whose value is invariably low or whose low-value applications can be identified with high reliability. For other services, however, more sensitive measures could result in unintended restriction of appropriate tests and procedures by coverage and payment policies, while more specific measures could substantially limit the impact of these strategies. Provider groups seeking to minimize wasteful spending, for example in response to global budgets, may be able to distinguish appropriate from inappropriate practices at the point of care without having to employ rigid rules derived from incomplete clinical data.

We also found that, although spending on low-value services varied considerably across regions, spending on low-value services was substantial even in regions where it was lowest. For example, low-value spending at the 5^th percentile of the regional distribution of low-value spending was greater than the difference in low-value spending between the 5^th and 95^th percentiles. This finding suggest potential advantages of direct measurement over relative spending comparisons as a basis for detecting overuse because overuse may be substantial even among more efficient providers.

Our study has several limitations. Most notably, we analyzed only 26 measures of low-value services. In selecting these measures, we emphasized the specificity with which overuse could be detected with claims data and created more restrictive versions that limited contributions of potentially valuable service use to low-value spending totals and utilization counts. Despite the limited number of services we examined, their frequency and correlations with one another suggest substantial and widespread wasteful care. Use of a broader set of less specific and more sensitive measures would capture more low-value care. Similarly, broader definitions of wasteful spending that include downstream costs of low-value service use (e.g., repeat imaging for incidental findings) would capture more spending than our measures did. For example, one study estimated that testing costs may account for just 2% of the lifetime costs of PSA screening.¹⁴

Clinical data from linked medical records might support a more extensive assessment of the properties of claims-based measures. However, we would not expect the incorporation of more detailed data to substantially alter the amount of low-value care captured by many of our measures (e.g. cancer screening above certain ages, inappropriately frequent bone mineral density testing, homocysteine testing for cardiovascular disease, renal artery stenting, and vertebroplasty). Furthermore, by varying the definitions of our measures, we were able to demonstrate potential limitations of claims-based measures without having to use medical record data; any inconsistencies between claims and medical records in the amount of low-value care detected would have similar implications for strategies to address wasteful practices. Moreover, we focused on the potential utility of claims-based measures because medical record review as a means to measure and monitor wasteful care is costly and thus not feasible on a large scale. Nevertheless, validation of claims-based measures against a gold standard of clinical appropriateness will be needed to more precisely define their strengths and weaknesses and assess their utility for different purposes, such as monitoring, profiling, payment policy, or coverage design.

Although our analysis suggests that common drivers of low-value care exist, our study did not identify specific determinants of wasteful care. Factors associated with low-value care also may be associated with high-value care.^15,16 Coupling measures of overuse with measures of underuse may therefore be important when evaluating programs intended to achieve more cost-effective care.

Finally, unmeasured variation in diagnostic coding practices or case mix may have contributed to positive correlations between regional use of different low-value services in our study. These were not likely sources of significant bias, however, because we found a significant positive correlation between categories of low-value services that did not rely on diagnosis codes to define (i.e. age-inappropriate cancer screening and preoperative testing) and because our results were not sensitive to adjustment for additional conditions qualifying beneficiaries for potential receipt of several low-value services.

Many quality measures have been developed to assess underuse but few to assess overuse. Our study illustrates the potential utility and limitations of a direct approach to detecting wasteful care. Despite their imperfections, claims-based measures of low-value care could be useful for tracking overuse and evaluating programs to reduce it. However, many direct claims-based measures of overuse may be insufficiently accurate to support targeted coverage or payment policies that have a meaningful impact on use without resulting in unintended consequences. Broader payment reforms such as global or bundled payment models could allow greater provider discretion in defining and identifying low-value services while incentivizing their elimination.