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Indices for Health Utilization and Loss Ratios: Benchmarks from the Health
Insurance Industry
Dr. Etti G. Baranoff, FLMI* Associate Professor of Insurance and Finance
Department of Finance, Insurance and Real Estate School of Business
Virginia Commonwealth University Snead Hall,
301 West Main Str., Suite B4167 Richmond VA 23284-4000
Cell: 512-750-6782 www.professorofinsurance.com
[email protected] or [email protected]
Dr. Dalit Baranoff Fellow at the Johns Hopkins University Institute for Applied Economics, Global
Health, and the Study of Business Enterprise Principal Researcher, Insurance Historian
Risk and Consequences [email protected]
(301)949-2590
Professor Thomas W. Sager, PHD Professor of Statistics
Department of Information, Risk, and Operations Management The University of Texas at Austin, CBA 5.202
Austin, Texas 78712-1175 (512) 471-3322
Dr. Bo Shi Associate Professor of Finance
School of Business and Public Affairs Morehead State University
Morehead, Kentucky [email protected]
(606)783-2475
*Corresponding author For 2018 FMA meeting
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Indices for Health Utilization and Loss Ratios: Benchmarks from the Health
Insurance Industry
Proposal for 2018 FMA meeting Please, do not quote Short Abstract: Using the U.S. Health insurance industry data, we create indices for utilization and loss ratios by insured sub-populations for 2006-2014. The indices may be used as general benchmarking standards. Objectives Our objective is to provide broadly-based health care utilization and loss ratio indices that can be used to corroborate health studies and to inform policy choices. The indices also provide context for value-based analyses in health care models. Study Design A careful review of the literature confirmed a dearth of broad findings on trends and indices of healthcare utilization (i.e., provider encounters, hospital admissions, and days in hospital), across sub-populations of insureds: Comprehensive (i.e., the working sub-population in group and individual coverage), Federal, Medicaid, and Medicare. We collected data from U.S. health insurers to fill this gap. Methods To create the indices, we first computed the rate of utilization per member per month (PMPM) for the entire U.S. health insurance industry as a whole separately for provider encounters, for hospital admissions, for duration of hospitalization, and for loss ratios. Then these rates were converted into indices by dividing the rate for each year by the rate for 2010, making 2010 the base year with index value = 1.00. By a corresponding process, rates and indices were computed for each individual U.S. health insurer. We also provide a case study of a single insurer. Results Temporal trends in some industry indices are contrasting. Most dramatically, days in hospital utilization for the Medicaid sub-population doubled between 2006 and 2014. On the other hand, hospital admissions for the Medicare sub-population declined substantially over nearly the same time period. By contrast, outpatient encounter utilization for Medicare has increased since 2012. Encounter utilization for Medicaid is variable. Comprehensive coverage for the working sub-population of group and individual utilizations has remained fairly stable since 2010. Medical loss ratio indices declined until 2010 then increased. Conclusions
3
Over time, we find some dramatic changes, but the patterns are not uniform across the utilization and sub-population indices. The different utilization and sub-population indices for the industry inform policy choices, health studies, and value-based analyses. The same indices for individual insurers provide benchmarks for
various stakeholders. Takeaways Points Industry-wide and insurer-specific indices of health utilization and loss ratios are useful
for evaluating and comparing trends in healthcare studies. Industry-wide indices provide
broadly-based context for policy decisions and value-based analyses. Insurer-specific
indices provide benchmarks for evaluation of insurer performance. Empirically, indices
for three health utilizations and one loss ratio for five sub-populations reveal contrasting
trends that are of interest in themselves.
4
Indices for Health Utilization and Loss Ratios: Benchmarks from the Health
Insurance Industry
I. Introduction
Using the health insurance industry’s own data, we create indices for healthcare
utilization and medical loss ratios for the period 2006-2014, using 2010 as the base year.
The indices quantify encounters with providers, admission to hospitals, and duration of
hospital stays for each of the five major insured sub-populations of individually covered
insureds, insureds covered by employer group comprehensive health insurance plans,
Federal employees, Medicaid and Medicare beneficiaries.i
Medical utilization and loss ratio indices can have several major uses. One is to monitor
trends in healthcare expenses (loss ratios) and utilization over time. Previous work on
healthcare utilization is limited in scale or scope, typically focusing on a specific sub-
population, geographic area, or disease (such as diabetes). But without benchmarks to
compare against, the results of these various studies remain limited. They cannot (and
should not) be generalized. Our indices provide a tool for validating results and placing
them within a larger context. As policymakers face decisions about the future of the
ACA, developing an accurate “big picture” of healthcare utilization will only grow in
importance.
A second major use is to strengthen value-based healthcare studies. Value-based studies
attempt to assess the value added of various new procedures, drugs, and co-pay
incentives in relation to the value and cost of alternatives. Our indices would provide
wider context for such studies to evaluate their validity relative to industry’s standards
rather than just provide stand-alone results.
A third use is to provide system-wide benchmarks for payers (individuals or employers)
to compare and select insurers. For example, an employer could compare the frequency
of health utilization among competing insurers with his/her own employee data to better
select an appropriate insurer or to evaluate its own situation relative to the sub-
populations. Also, an insurer can compare its performance to that of the industry or other
insurers specializing in similar niche markets. Section V shows an example of
benchmarking for an actual insurer.
The health insurance industry is the financial intermediary of the US healthcare system,
collecting premiums from consumers and dispensing them to providers. In that position,
health insurers have amassed considerable power to manage the care available to
consumers through insurers’ ability to authorize or to withhold payment. Health insurers
collect vast quantities of data on their customers’ utilization of medical services. Health
insurers report those data in aggregate form, broken down at various levels of granularity,
i Indices are used in business and economics to make comparisons and provide benchmarks. A widely
familiar example is the Consumer Price Index, which compares the cost of a market basket of goods and
services in one year to the cost of the same market basket in a base year. An index is a ratio of two related
economic numbers, which expresses the numerator as a percentage of the denominator, called the base.
5
in their annual reports filed with the National Association of Insurance Commissioners
(NAIC). We used this data to compute our indices for the years bracketing the
introduction of the ACA (2006-2014). Our base year (2010) was the first year of ACA
implementation.
Results show that index trends vary substantially, depending upon the type of utilization
and the sub-population of insureds. Most dramatically, duration of hospitalization for the
Medicaid sub-population doubled between 2006 and 2014. Over nearly the same time
period, hospital admissions for the Medicare sub-population declined substantially. By
contrast, outpatient encounters for Medicare have increased since 2012. Medicaid tends
to follow the trend for Medicare in encounters, but deviates substantially for hospital
admissions and durations. Duration of hospitalization remained fairly stable for the
individual and employer group sub-populations. Medical loss ratios declined until 2010
then increased.
In a case study, we compare a specific index for one insurer to the industry or sub-
population index.
Following this introduction, the paper continues with examples of studies that we relate
to the use of the indices we develop. We provide these examples to illustrate the added
value that indices can provide. Section III describes the data and the method we use,
while Section IV shows and explains the resulting indices from 2006 to 2014. The case
study for one insurer is provided in Section V. The paper closes with conclusions and
takeaways.
II. Literature which can be related to the indices
In this section, we review the literature on trends in healthcare utilization, identifying
studies that could be compared with our benchmarks. In particular, we focus on studies
related to ACA provisions to see if their specific results correlate with the larger trends
shown by our indices for the years bracketing healthcare reform. It is important to note,
however, that our purpose is not to identify studies that confirm (or negate) our
benchmarks, but rather to demonstrate how our indices can be used to fit such studies into
a larger context.
We found that most scholarship focuses on provisions implemented immediately after the
passage of the ACA (2010, 2011) such as expanded coverage for young adults and
preventative care requirements. Studies of provisions implemented later tend to rely on
small case studies or tend to be narrowly focused. Despite their limited scale and scope,
these studies are useful in that their data and results can be compared to our observations.
One of the most widely studied provisions of the ACA, commonly referred to as the
“young adult mandate,” allows adult children to remain as dependents on their parents’
health insurance coverage through age 26 (this applies to all individual and group
policies). Studies of this provision look at changing utilization by young adults following
ACA implementation and/or compare utilization of specific services, such as emergency
6
department (ED) use, hospitalization, and inpatient mental health services, by different
age cohorts. Results indicate increased inpatient visits by young adults 26 and under, with
a significant rise in mental health-related inpatient care.1 Scholars have also associated
expanded young adult coverage with a decrease in ED utilization.2 Although our indices
do not separate utilization by age, these findings are consistent with our Figure 2 (in
Section IV), which shows a small growth in inpatient care from 2010 to 2011 for group
comprehensive coverage. This demonstrates the use of our indices for comparative
analysis of specific research studies.
The other major ACA provision taking effect in 2010 was the elimination of patient cost-
sharing for preventive care and cancer screenings, such as colonoscopy and
mammography for private insurance coverage. A similar provision for Medicare began in
2011. Studies exploring whether utilization for these services changed after the
elimination of cost-sharing produce mixed results. Increases in tests for blood pressure
and cholesterol and in flu vaccinations were seen among the privately insured, ages 18-
64.3 However, no similar utilization change was found for cancer screenings such as
colonoscopies and mammography following implementation of the provision.4
Preventative care and screenings are both measured by our indices for “encounters”. For
the working sub-population, the encounters indices showed an up-tick from 2010 to 2011
for those covered by group health insurance, but not for those with individual coverage
(see Figure 1). The results of the example studies here could be corroborated with the
indices.
Studies of Medicare beneficiaries produce similar results, showing no or only a small
increase in utilization of preventative and/or cancer-screening services in the year
following cost-sharing elimination and free annual exams.5,6 Our encounters index for
Medicare (Figure 1) supports this specific finding in that the index remained level until
2012, after which it rose steeply.
Another key provision of the ACA is Medicaid expansion for childless adults with
incomes up to 133% of the federal poverty level. This provision was not fully
implemented in 2010, but some states began as early as 2010. Case studies of early
expansion states showed increases in Medicaid enrollment.7 These results correspond
with our findings for the larger Medicaid sub-population served by the health insurance
industry (see Figure 1).
With 19 states choosing not to participate in Medicaid expansion (as of 2016), we also
find comparisons of health outcomes in expansion and non-expansion states. Many
studies narrow their examinations to topics such as the effects of Medicaid expansion on
HIV patients, cancer, mental health care, and behavioral health. 8, 9, 10, 11, 12 As would be
expected, coverage increased, but changes in utilization varied, corroborating with the
indices (see Figures 1-3).
Most of the ACA-related studies are consumer focused, but we also found relevant
scholarship with an insurer or payer perspective. For the payers’ perspective we look to
7
studies of “value-based” healthcare models, in which stakeholders seek to evaluate
whether various procedures, care management, and methods of co-pays add value to
health outcomes. The indices we create can contribute to the value-based scholarship by
providing standards (developed from the health insurance industry-wide data) for use in
comparison of outcomes.
Because our indices provide “norms” for utilizations by different sub-population, relevant
value-based studies include those on drug costs and usage and the effects of copays (or
their elimination) on utilization and health outcomes.13,14,15 While our indices do not
address these types of studies directly, we can evaluate their results in relation to our
indices measuring healthcare utilizations under the “encounters” category and our loss
ratios indices.
Scholars also investigate models for management of chronic diseases such as
diabetes.16,17 Receiving particular attention of late are studies of the effect of patient-
centered medical home (PCMH) models on healthcare utilization by various sub-
populations (such as the chronically ill or the elderly).18,19,20,21,22 One aim of the PCMH
model is to transfer patient healthcare utilization from hospital to out-patient care. But
one or two studies showing a trend in this direction have limited value. Comparing the
results of these individual studies to our indices for hospital admissions, days in the
hospital, and encounters, however, places it within a larger framework.
III. Data and the Method of Creating the Indices
We used the data from the annual statements of all US health insurers filing with the National Association of Insurance Commissioners (NAIC). The data are very rich regarding utilizations and loss ratios within sub-populations. For each health insurer
for each year, we computed the rate of utilization separately for provider encounters, for hospital admissions, and for duration of hospitalization as the total specific utilizations in a year ÷ (number of insureds x number of months of coverage). The rate is
therefore the frequency of use expressed as per member per month (PMPM). All reported
encounters with medical providers were included – physicians and non-physicians. Similarly, we computed the severity of utilization for each insurer as a medical loss ratio:
total medical utilization expenses incurred ÷ total health insurance premiums collected.
These indices were calculated separately for five non-overlapping insured sub-
populations covered by group and individual comprehensive health insurance coverage,
Federal, Medicaid, and Medicare coverages.
Aggregate industry-wide utilization and severity rates and indices were also computed
correspondingly after combining all insurers as though the industry were a single insurer.
To provide an additional combined sub-populations (or “grand industry”) metric, the
utilization and severity indices were also calculated for the combination of all five sub-
populations, as though they were a single population.
8
IV. Results
In this section the indices are displayed graphically over time for the years 2006-2014.
There are five insured sub-populations for each of the three utilization indices, and for
each we include the combined sub-population index – the grand industry index. The same
is done for the loss ratios with group and individual coverage combined. Figures 1-4
show, respectively, the three utilization metrics and the loss ratio, with all insured sub-
populations overlaid on each graph and the grand industry index for each.
Analysis of the indices in Figures1-4 reveals several notable trends. Most dramatically,
they show a doubling in “days in the hospital” utilization for the Medicaid sub-population
between 2006 and 2014 (Figure 3). Over nearly the same time period, hospital
admissions for the Medicare sub-population declined substantially (Figure 2). By
contrast, outpatient encounter utilization for Medicare has grown since 2012 (Figure 1).
Encounter utilization for Medicaid is variable. Individual and group coverage (the
working sub-population) utilization has remained fairly stable since 2010. For all sub-
populations combined, medical loss ratio indices declined until 2010 then increased until
2013, after which they declined sharply through 2014 (Figure 4).
Figure 1
The grand industry or “all sub-populations combined” index for encounters shows an
initial sharp increase, followed by a modest leveling off for several years, and then
another sharp increase from 2012 to 2014. In total, it climbs from about 0.90 in 2006 to
about 1.12 in 2014 – a substantial increase). The individual health insurance coverage
sub-population is the only group showing (slightly) lower index at the end than at the
beginning.
Our study is not designed to address why individuals have been seeking medical attention
more often. In Section II we show some possible connections between the literature cited
9
and the indices to demonstrate how specific results can be corroborated with the general
indices for additional confirmation. Since the indices encompass the period of the Great
Recession and the ACA, they can also be used to validate research on the effects these
macro events may have had on healthcare utilization.
For example, some recent reports suggest that newly insured individuals have greater
health problems than the already insured population.23,24 If true, we would expect to see
increased utilization by the individually insured population after 2010 (when pre-existing
condition exclusions were eliminated).ii For encounters, however, we see an overall
decline between 2010 and 2014. The only years the index rises are between 2012 and
2013. Where we do see evidence of more sick individuals in the individually insured
group is in the indices for hospital admissions (Figure 2) and days in the hospital (Figure
3), both of which rose steeply between 2013 and 2014.
Another place we might expect to see evidence that the newly insured are sicker is in the
Medicaid utilization indices. Both the 2008 recession and ACA Medicaid expansion
brought additional people into the Medicaid system. For encounters, however, the results
are not so clear. We see a major increase in encounters between 2006 and 2008 and again
between 2012 and 2013, and a modest rise from 2010 to 2011, but other years show
decline.
In Baranoff, et al (2016) we show that the membership in employer-provided group
health insurance declined dramatically since 2008, in association with a large reduction in
employment.25 Despite the lower numbers, the group health index for encounters
increased, indicating that group insured (i.e. employed) individuals visited the doctor
more during the recession. However, this trend is not reflected in the indices for hospital
admissions (Figure 2) or days in the hospitals (Figure 3) for the group health coverage.
Overall effects of both the Great Recession and the beginning of the ACA are suggested
in the “all sub-population combined” index. Here we see an initial increase in encounters
starting in 2008 with the beginning of the recession, followed by a leveling off, and then
after 2010, we see another increase associated with the beginning of the ACA.
Figure 2
10
Examination of Figure 2 shows that inpatient admissions for the industry as a whole (“all
sub-populations combined”) initially rises, then levels off, then rises again. The five
individual sub-populations reveal more idiosyncratic trends for inpatient admissions than
for encounters. All initially increase, although the rise for Federal employees is delayed.
An exception is the group coverage sub-population, which remains level until 2009, at
which point it drops significantly. In contrast, the index trend for Federal employees
shows an increase between 2008 and 2010, followed by a decrease.
The individual coverage index reflects some changes that may be related to the ACA and
the beginning of the exchanges in late 2013. Before 2010, after an initial peak of 1.38, the
individual coverage index declines to about 0.93 before jumping back to 1.15 in 2014.
The decline in the group coverage index, which fell from about 1.13 (in 2006) to about
1.00 (in 2014) may also be linked to the ACA.
After 2010, the initial ACA year, the index for admission for Medicare declined, perhaps
reflecting an expanded use of managed care or a waning of recession-related health
problems.26,27 Medicare shows a sharper and less steady decline from 1.15 in 2007 to
about 0.88 in 2014. Medicare and Medicaid diverge, however, with Medicaid going from
substantially more utilization than Medicaid to substantially less by 2014. Some of the
same factors noted as possibly at work in the discussion of encounters may be at work
here: Initial rises in utilization are co-temporal with the coming of the Great Recession,
while increases in admissions at the end of the period coincide with expansion of
Medicaid eligibility.
Figure 3
0.8500
0.9500
1.0500
1.1500
1.2500
1.3500
2006 2007 2008 2009 2010 2011 2012 2013 2014
Per Member Per Month Inpatient Admissions Indices
Individual Coverage
Group Coverage
Federal Employees
Medicare
Medicaid
All Populations Combined
11
Although we expected the utilization indices of “days in the hospital” in Figure 3 to
closely follow the admission indices in Figure 2, the patterns diverge. The grand
industry index shoots upward, from about 0.85 to about 1.42. But the greatest
increase is in the Medicaid index, which doubles. The other sub-populations show a
fairly consistent pattern in the duration of hospitalization index: Initially, slightly up
(although Federal is again delayed), followed by decline and leveling off, followed by
modest rises.
Medicaid meanwhile, experienced a significant increase, with a doubling of per
member utilization since 2006. This increase may reflect a sicker population, or it
could be the result of some other unknown factor. Once again, Medicaid switched
relative position with Medicare, going from less utilization than Medicare at the
beginning to more at the end. Medicare actually finished the period slightly lower
than it began. Federal is the only other sub-population that finished below its start.
Figure 4
0.8000
0.9000
1.0000
1.1000
1.2000
1.3000
1.4000
1.5000
1.6000
2006 2007 2008 2009 2010 2011 2012 2013 2014
Per Member Per Month Patient Days Indices
Individual Coverage
Group Coverage
Federal Employees
Medicare
Medicaid
All Populations Combined
12
With figure 4 we move to the loss ratio index, which we use to represent the severity
index for health care expenditures. Compared with the utilization indices, the medical
loss ratio index is very stable across the sub-populations. The vertical scale in Figure 4 is
exaggerated to show the variation. The patterns are also fairly consistent among the sub-
populations, as well as for all the sub-populations combined: rise-decline-rise-decline,
except for Medicare, which tends to increase.
V. How to use the indices? - Demo
In this section, we provide a case study of one real health insurer, herein called “Acme”,
to illustrate the use of our indices. Columns 2-5 of Table 1, panel A, show raw data for
Acme’s group insurance line: total member months, total encounters, total hospital
admissions, and total days of hospitalization. Columns 6-8 (in yellow) show Acme’s
corresponding PMPM figures, obtained by dividing encounters, admissions, and days by
member months. Columns 9-11 (in blue) display the group line of the whole health
insurance industry’s corresponding PMPM figures. For every year except 2006, all of
Acme’s PMPM rates are less than the corresponding industry utilization rates.
Indices are useful here to make a relative comparison of Acme to the industry. Panel B
shows the utilization indices for both Acme and the industry. Indices are obtained by
dividing PMPM utilization for a year by PMPM utilization for 2010 (the base year). For
example, the encounter index for Acme in 2014 = 0.5350 (Acme 2014 encounter PMPM)
÷ 0.4475 (Acme 2010 encounter PMPM) = 1.1957. Thus, Acme’s group encounter
utilization was nearly 20% higher in 2014 than it was in 2010. By contrast, the industry’s
group encounter utilization was only 4.35% higher in 2014 than in 2010.
0.9900
0.9950
1.0000
1.0050
1.0100
1.0150
1.0200
1.0250
1.0300
1.0350
2006 2007 2008 2009 2010 2011 2012 2013 2014
Medical Loss Ratio Indices
Group and Individual
Coverage
Federal Employees
Medicare
Medicaid
All Populations Combined
13
Further perusal of the indices shows that Acme began the period with very high group
utilization rates relative both to the industry and to itself. All Acme utilizations declined
substantially until about 2010, when encounters and days turned back up, whereas
admissions continued to decrease (after an anomalous 2011). By contrast, the industry for
group line experienced a much less pronounced irregular increase in encounters and days,
and decrease in admissions. We are not privy to the corporate policy making that might
account for Acme’s utilization trends. But it is a reasonable inference that there must
have been a considerable emphasis on reducing Acme’s utilization rates from near
industry levels to significantly below – until 2010. Employers pondering the purchase of
group health insurance for employees might applaud these trends if they were to lead to
lower premiums, although employees might raise doubts. After 2010, there was
significant recovery of utilization rates, except for admissions. It would be interesting to
listen in on policy discussions relating to the post-2010 challenges.
Table 1.
VI. Conclusions In a survey of the literature, we found a lack of broad findings on indices and trends in
healthcare utilization (i.e., provider encounters, hospital admissions, and days in
hospital). To fill this gap, we use U.S. Health insurance industry data to create indices for
utilization and loss ratios by insured subpopulations for 2006-2014. Our objective is to
provide a broad context to evaluate health studies and to benchmark insurer performance.
DATA FOR INSUREDS COVERED BY GROUP INSURANCE ISSUED BY ACME
PANEL A Acme Acme Acme Industry Industry Industry
Acme Acme Acme Acme Encounters Admissions Days Encounters Admissions Days
Year Mem Months Encounters Admissions Days PMPM PMPM PMPM PMPM PMPM PMPM
2006 507,797 327,560 3,271 14,802 0.6451 0.0064 0.0291 0.7189 0.0059 0.0234
2007 926,995 477,672 5,242 21,623 0.5153 0.0057 0.0233 0.7591 0.0058 0.0249
2008 1,336,109 619,702 6,970 27,886 0.4638 0.0052 0.0209 0.7812 0.0057 0.0245
2009 1,615,502 733,640 6,992 30,138 0.4541 0.0043 0.0187 0.8022 0.0057 0.0267
2010 1,683,577 753,319 6,680 26,458 0.4475 0.0040 0.0157 0.7872 0.0052 0.0245
2011 1,726,717 704,225 8,810 34,627 0.4078 0.0051 0.0201 0.8238 0.0054 0.0258
2012 2,214,900 1,202,691 8,569 37,745 0.5430 0.0039 0.0170 0.7936 0.0053 0.0257
2013 2,430,546 1,298,998 8,663 53,636 0.5344 0.0036 0.0221 0.7705 0.0053 0.0260
2014 2,223,205 1,189,419 7,935 46,782 0.5350 0.0036 0.0210 0.8214 0.0053 0.0262
PANEL B Acme Acme Acme Industry Industry Industry
Encounters Admissions Days Encounters Admissions Days
Year INDEX INDEX INDEX INDEX INDEX INDEX
2006 1.4416 1.6235 1.8548 0.9132 1.1214 0.9545
2007 1.1516 1.4252 1.4843 0.9643 1.1019 1.0161
2008 1.0366 1.3148 1.3281 0.9923 1.0905 0.9969
2009 1.0149 1.0908 1.1871 1.0191 1.0938 1.0867
2010 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
2011 0.9115 1.2859 1.2761 1.0465 1.0314 1.0514
2012 1.2135 0.9751 1.0844 1.0081 1.0175 1.0479
2013 1.1944 0.8983 1.4042 0.9788 1.0151 1.0593
2014 1.1957 0.8995 1.3390 1.0435 1.0036 1.0672
14
We create both industry-wide and insurer-specific rates (frequency) of utilization per
member per month (PMPM) separately for provider encounters, for hospital admissions,
for duration of hospitalization, and for loss ratios. The final utilization rates and severity
were converted into indices by dividing each year by the rate or severity for 2010,
making 2010 the base year with index value = 1.00. To demonstrate how the indices can
be used to evaluate an individual insurer, we also provide a case study.
The findings of our study reveal that indices show contrasting trends. The main results
show that:
• Days in hospital utilization for the Medicaid population doubled between 2006
and 2014.
• Hospital admissions for the Medicare population declined substantially between
2006 and 2014.
• Outpatient encounter utilization for Medicare has increased since 2012.
• Encounter utilization for Medicaid is variable.
• Comprehensive coverage for the working population of group and individual
utilizations has remained fairly stable since 2010.
• Medical loss ratios indices declined until 2010 then increased.
These indices can be used to create context for other studies, to provide benchmarks for
choices by buyers, to help policymakers see the reality of the health care system by
providing greater transparency, and to discover trends.
15
Indices for Health Utilization and Loss Ratios: Benchmarks from the Health
Insurance Industry
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