Beginning in 2014, individuals and small businesses are able to purchase private health insurance through competitive Marketplaces. The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the second of three in this issue of the Review that describe the Department of Health and Human Services (HHS) risk adjustment methodology and focuses on the risk adjustment model. In our first companion article, we discuss the key issues and choices in developing the methodology. In this article, we present the risk adjustment model, which is named the HHS-Hierarchical Condition Categories (HHS-HCC) risk adjustment model. We first summarize the HHS-HCC diagnostic classification, which is the key element of the risk adjustment model. Then the data and methods, results, and evaluation of the risk adjustment model are presented. Fifteen separate models are developed. For each age group (adult, child, and infant), a model is developed for each cost sharing level (platinum, gold, silver, and bronze metal levels, as well as catastrophic plans). Evaluation of the risk adjustment models shows good predictive accuracy, both for individuals and for groups. Lastly, this article provides examples of how the model output is used to calculate risk scores, which are an input into the risk transfer formula. Our third companion paper describes the risk transfer formula.
BACKGROUND:In 2004, Medicare implemented a system of paying Medicare Advantage (MA) plans that gave them greater incentive than fee-for-service (FFS) providers to report diagnoses.DATA:Risk scores for all Medicare beneficiaries 2004-2013 and Medicare Current Beneficiary Survey (MCBS) data, 2006-2011.MEASURES:Change in average risk score for all enrollees and for stayers (beneficiaries who were in either FFS or MA for two consecutive years). Prevalence rates by Hierarchical Condition Category (HCC).RESULTS:Each year the average MA risk score increased faster than the average FFS score. Using the risk adjustment model in place in 2004, the average MA score as a ratio of the average FFS score would have increased from 90% in 2004 to 109% in 2013. Using the model partially implemented in 2014, the ratio would have increased from 88% to 102%. The increase in relative MA scores appears to largely reflect changes in diagnostic coding, not real increases in the morbidity of MA enrollees. In survey-based data for 2006-2011, the MA-FFS ratio of risk scores remained roughly constant at 96%. Intensity of coding varies widely by contract, with some contracts coding very similarly to FFS and others coding much more intensely than the MA average. Underpinning this relative growth in scores is particularly rapid relative growth in a subset of HCCs.DISCUSSION:Medicare has taken significant steps to mitigate the effects of coding intensity in MA, including implementing a 3.4% coding intensity adjustment in 2010 and revising the risk adjustment model in 2013 and 2014. Given the continuous relative increase in the average MA risk score, further policy changes will likely be necessary.
Objective: Document trends in time to post-discharge follow-up visit for Medicare patients with an index admission for heart failure (HF), acute myocardial infarction (AMI), or community-acquired pneumonia (CAP).Determine factors predicting whether the first post-discharge utilization event is a follow-up visit, treat-and-release emergency department (ED) visit, or readmission.Methods: Using Medicare claims data from 2007-2010, we plotted annual cumulative incidence functions for the time frame post-discharge to follow-up visit, accounting for competing risks with censoring at 30 days.We used multinomial probit regression to determine factors predicting the probability of first-occurring post-discharge utilization events within 30 days. Results:For each cohort, the cumulative incidence of follow-up visits increased during the study period.For example, in 2010, 54.6% of HF patients had a follow-up visit within 10 days of discharge compared to 47.9% in 2007.Within each cohort, the largest increase in follow-up visits took place between 2008 and 2009.Follow-up visits were less likely for patients who were Black, Hispanic, and enrolled in Medicaid or Medicare Advantage, and they were more likely for patients with greater comorbidities and prior procedures as well as those with private or supplemental Medicare coverage.There were no changes in 30-day readmission rates.Discussion: Although increases in follow-up visits may have been inf luenced by the introduction of publicly reported readmission rates in 2009, these increases did not continue in 2010 and were not associated with a change in readmissions.Patients who were Black, Hispanic, and/or enrolled in Medicaid or Medicare Advantage were less likely to have follow-up visits.
OBJECTIVE:The primary aim is to explore whether prescription drug expenditures by enrollees changed in Alabama's CHIP program, ALL Kids, after copayment increases in fiscal year 2004. The subsidiary aim is to explore whether non-pharmaceutical expenditures also changed.DATA SOURCES:Data on ALL Kids enrollees between 1999-2007, obtained from claims files and the state's administrative database.STUDY DESIGN:We used data on children who were enrolled between one and three years both before and after the changes to the copayment schedule, and estimate regression models with individual-level fixed effects to control for time-invariant heterogeneity at the child level. This allows an accurate estimate of how program expenditures change for the same individual following copayment changes. Primary outcomes of interest are expenditures for prescription drugs by class and brand-name and generic versions. We estimate models for the likelihood of any use of prescription drugs and expenditure level conditional on use.PRINCIPAL FINDINGS:Following the copayment increase, the probability of any expenditure decline by 5.8%, brand name drugs by 6.9%, generic drugs by 7.4%. Conditional on any use, program expenditures decline by 7.9% for all drugs, by 9.6% for brand name drugs, and 6.2% for generic drugs. The largest declines are for antihistamine drugs; the least declines are for Central Nervous System agents. Declines are smaller and statistically weaker for children with chronic health conditions. Concurrent declines are also seen for non-pharmaceutical medical expenditures.CONCLUSIONS:Copayment increases appear to reduce program expenditures on prescription drugs per enrollee and may be a useful tool for controlling program costs.
OBJECTIVE Determine the association between access to primary care by the underserved and Medicare spending and clinical quality across hospital referral regions (HRRs). DATA SOURCES Data on elderly fee-for-service beneficiaries across 306 HRRs came from CMS' Geographic Variation in Medicare Spending and Utilization database (2010). We merged data on number of health center patients (HRSA's Uniform Data System) and number of low-income residents (American Community Survey). STUDY DESIGN We estimated access to primary care in each HRR by "health center penetration" (health center patients as a proportion of low-income residents). We calculated total Medicare spending (adjusted for population size, local input prices, and health risk). We assessed clinical quality by preventable hospital admissions, hospital readmissions, and emergency department visits. We sorted HRRs by health center penetration rate and compared spending and quality measures between the high- and low-penetration deciles. We also employed linear regressions to estimate spending and quality measures as a function of health center penetration. PRINCIPAL FINDINGS The high-penetration decile had 9.7% lower Medicare spending ($926 per capita, p=0.01) than the low-penetration decile, and no different clinical quality outcomes. CONCLUSIONS Compared with elderly fee-for-service beneficiaries residing in areas with low-penetration of health center patients among low-income residents, those residing in high-penetration areas may accrue Medicare cost savings. Limited evidence suggests that these savings do not compromise clinical quality.
OBJECTIVE:To describe the characteristics of hospitalists serving Medicare beneficiaries.DATA SOURCES:Medicare claims from 2009 and 2011 merged with the Provider Enrollment, Chain, and Ownership System file for physician characteristics.STUDY DESIGN:Our construction of the Medicare Data on Physician Practice and Specialty (MD-PPAS) enabled identification of hospitalists based on the attending physician for Medicare admissions (medical and surgical) in 2009 and 2011.PRINCIPAL FINDINGS:In 2011, hospitalists constituted 13.3% of physicians who designated their specialty as primary care and 4.4% of all physicians serving Medicare beneficiaries. Compared to other physicians, hospitalists were more likely to be female, under forty, and in large practices. More than a quarter of Medicare admissions had a hospitalist as the attending physician, though the rate was substantially higher for medical than surgical admissions (31.8% versus 11.3%). Between 2009 and 2011, the percentage of medical admissions with a hospitalist as the attending physician increased by roughly a quarter (from 25.7% to 31.8%).CONCLUSIONS:This analysis provides a more current and complete estimate of the use of hospitalists by the Medicare population than is available from prior studies. The ability to identify hospitalists from claims data will facilitate research on the impact of hospitalist use on quality and cost.
Objective: Potentially avoidable hospitalizations have been identified by experts as leading to poor health outcomes and costly care.Potentially avoidable hospitalizations are particularly common among full-benefit dual eligible beneficiaries.This paper examines potentially avoidable hospitalizations rates by setting, state, and medical condition, and the average cost of these events.Methods: This analysis identifies potentially avoidable hospitalizations using diagnosis codes identified by an expert panel.Settings of care are determined using a timeline file, which assigns an individual to a specific setting on a particular day.Population/Data Source: The analysis uses several different datasets from the Chronic Conditions Data Warehouse.The study population includes fee-for-service beneficiaries who were eligible for both Medicare and full Medicaid benefits for at least one month during the calendar year.The study years are 2007 to 2009. Results:In 2009, among our study population, 26 percent of hospitalizations were potentially avoidable; and the rate was 133 per 1,000 person-years.Potentially avoidable hospitalizations were much more likely for those beneficiaries who were in institutions-16 percent of beneficiaries in our study population were in an institution, yet comprised 45 percent of all potentially avoidable hospitalizations.The range in rates across the states was considerable, with more than a threefold difference across states.Five conditions were responsible for nearly 80 percent of potentially avoidable hospitalizations.From 2007 to 2009, the national and state rates were fairly consistent. Discussion:This analysis indicates that the potentially avoidable hospitalization rate among MME beneficiaries was consistently high from 2007 to 2009.This bears monitoring in the future to see if the Centers for Medicare & Medicaid Services' various initiatives have led to a reduction in rates.
OBJECTIVES:Under the Ticket to Work and Work Incentives Improvement Act (PL 106-170), states may extend Medicaid Buy-In coverage to a medically improved group. Improved group coverage allows adults with disabilities to retain Medicaid coverage even once they lose disability status due to medical improvement, as long as they retain the original medical impairment. The goal of this paper is to describe who participated, the patterns of their participation, and employment outcomes.METHODS:The study population consists of all individuals (n = 315) who participated in medically improved group coverage 2002-2009 in the seven states with coverage by 2009 (Arizona, Connecticut, Kansas, New York, North Carolina, Pennsylvania, and West Virginia). Linked data from state Medicaid Buy-In finder files and Social Security Administration Ticket Research and Master Earnings Files were used to describe improved group participants and their patterns of enrollment.RESULTS:Although enrollment has been limited, with 255 participants in 2009, it has doubled annually on average with little churning and drop-out. Participants' earnings grew nearly 200 dollars per month after two years, likely reflecting increased work hours and/or higher pay rates.CONCLUSIONS:Improved group participants represent an unusually successful group of individuals with disabilities, many of whom have recently moved off Social Security cash benefit rolls or who were diverted from them. Specifics of insurance eligibility and coverage for improved group participants are uncertain under the Affordable Care Act. The challenge remains to provide a pathway for adults with disabilities to increase work and assets without loss of adequate health insurance.
OBJECTIVE:Patient activation questions from a major national Medicare survey are used to highlight characteristics of Medicare beneficiaries with low activation. We demonstrate that Medicare Current Beneficiary Survey (MCBS) data is an untapped resource for further research on patient activation within Medicare beneficiaries and programs.DATA SOURCE:Data are from the 2012 MCBS Access to Care file and include 10,650 beneficiaries.METHODS:Patient Activation levels were derived by taking the weighted average responses to the Patient Activation Supplement. Cut points for high, moderate, and low activation were assigned at +/- ½ standard deviation of the mean. Data were analyzed using SAS survey procedures. Within group comparisons were tested using chi-square tests with post hoc pairwise comparisons. Logistic regression identified predictors of low patient engagement.RESULTS:In a multiple logistic regression, beneficiary characteristics associated with low activation included Hispanic origin, being widowed or never married, select age groups, male gender, fair or poor health, difficulty with an IADL or ADLs, and having no usual source of care, with failure to complete high school as the strongest predictor (OR=2.22, p<.001). Utilization and costs were also examined in descriptive analyses.DISCUSSION:Overall, findings on the characteristics of low activation patients in the Medicare population resemble previous research. In a regression analysis, less education and no usual source of care are the strongest predictors of low activation levels in Medicare beneficiaries. The MCBS Patient Activation Supplement is a rich resource for examining patient activation in the Medicare population, and can be used for a wide range of analyses.
RESEARCH OBJECTIVE:Hospital-acquired conditions, or HACs, often result in additional Medicare payments, generated during the initial hospitalization and in subsequent health care encounters. The purpose of this article is to estimate the incremental cost to Medicare, as measured by Medicare program payments, of six HACs.STUDY DESIGN:The researchers used a matched case-control design to determine the incremental increase in Medicare payments attributable to each HAC. For each HAC patient, five comparison patients were matched on diagnosis group, sex, race, and age. Using the matched sample, we estimated a hospital fixed effects log-linear regression on total Medicare payments for the episode of care, further controlling for co-morbid conditions. Care episodes included the initial hospitalization and all inpatient, outpatient, physician, home health, and hospice care that occurred within 90 days of hospital discharge.POPULATION STUDIED:All Medicare fee-for-service patients discharged alive from a hospital between October 2008 and June 2010 with one of six HACs-severe pressure ulcer, fracture, catheter-associated urinary tract infection, vascular catheter-associated infection, surgical site infection following certain orthopedic procedures, or deep vein thrombosis/ pulmonary embolism following certain orthopedic procedures-were included in the sample and matched to five similar patients without the HACs.PRINCIPAL FINDINGS:The multivariate analysis suggests that Medicare paid an additional $146 million per year across these HAC care episodes compared with what would have been paid without the HACs.CONCLUSIONS:HACs create a significant financial burden for the Medicare program. We compare the incremental Medicare payments for these six HACs to the current and upcoming Medicare HAC payment penalties.
OBJECTIVE Examine use of the Internet (eHealth) and mobile health (mHealth) technologies by privately insured, publicly insured (Medicare/Medicaid), or uninsured U.S. adults in 2012. DATA SOURCE Pew Charitable Trust telephone interviews of a nationally representative, random sample of 3,014 adult U.S. residents, age 18+. METHODS Estimate health information seeking behavior overall and by segment (i.e., insurance type), then, adjust estimates for individual traits, clinical need, and technology access using logistic regression. RESULTS Most respondents prefer offline to online (Internet) health information sources; over half across all segments use the Internet. More respondents communicate with providers offline compared with online. Most self-reported Internet users use online tools for health information, with privately insured respondents more likely to use new technologies. Unadjusted use rates differ across segments. Medicaid beneficiaries are more likely than the privately insured to share health information online, and Medicare beneficiaries are more likely than the privately insured to text with health professionals. After adjustment, these differences were minimal (e.g., Medicare beneficiaries had odds similar to the privately insured of online physician consultations), or the direction of the association reversed (e.g., Medicaid beneficiaries had greater odds than the privately insured of online physician consultations versus lower odds before adjustment). DISCUSSION Few adults report eHealth or mHealth use in 2012. Use levels appear unevenly distributed across insurance types, which could be mostly attributed to differences in individual traits and/or need. As out-of-pocket costs of medical care increases, consumers may increasingly turn to these generally free electronic health tools.
Beginning in 2014, individuals and small businesses will be able to purchase private health insurance through competitive marketplaces.The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect.The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge and the incentive for plans to avoid sicker enrollees.This article -the first of three in the Medicare & Medicaid Research Review-describes the key program goal and issues in the Department of Health and Human Services (HHS) developed risk adjustment methodology, and identifies key choices in how the methodology responds to these issues.The goal of the HHS risk adjustment methodology is to compensate health insurance plans for differences in enrollee health mix so that plan premiums reflect differences in scope of coverage and other plan factors, but not differences in health status.The methodology includes a risk adjustment model and a risk transfer formula that together address this program goal as well as three issues specific to ACA risk adjustment: 1) new population; 2) cost and rating factors; and 3) balanced transfers within state/market.The risk adjustment model, described in the second article, estimates differences in health risks taking into account the new population and scope of coverage (actuarial value level).The transfer formula, described in the third article, calculates balanced transfers that are intended to account for health risk differences while preserving permissible premium differences.
The Affordable Care Act provides for a program of risk adjustment in the individual and small group health insurance markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the third of three in this issue of the Medicare & Medicaid Research Review that describe the ACA risk adjustment methodology and focuses on the risk transfer formula. In our first companion article, we discussed the key issues and choices in developing the methodology. In our second companion paper, we described the risk adjustment model that is used to calculate risk scores. In this article we present the risk transfer formula. We first describe how the plan risk score is combined with factors for the plan allowable premium rating, actuarial value, induced demand, geographic cost, and the statewide average premium in a formula that calculates transfers among plans. We then show how each plan factor is determined, as well as how the factors relate to each other in the risk transfer formula. The goal of risk transfers is to offset the effects of risk selection on plan costs while preserving premium differences due to factors such as actuarial value differences. Illustrative numerical simulations show the risk transfer formula operating as anticipated in hypothetical scenarios.
BACKGROUND:Prior to the implementation of the Hospital-Acquired Condition-Present on Admission (HAC-POA) payment policy, concerns regarding its potential impact were raised by a number of organizations and individuals. The purpose of this study was to explore direct and indirect effects of the HAC-POA payment policy on hospitals, patients, and other payers during the policy's first 3 years of implementation.METHODS:The study included semi-structured telephone interviews with representatives of national organizations, hospitals, patient advocacy organizations, and other payers. Interview notes were coded using QSR NVivo qualitative analysis software using inductive and deductive qualitative analysis techniques. We conducted interviews with 106 individuals representing 56 organizations. Hospital staff included physicians, nurses, patient safety officers, coders, and finance, senior management, and information management staff. Individuals from other organizations represented leadership positions.RESULTS:Key changes to hospitals included: cultural shifts involving attention, commitment, and support from hospital leadership for patient safety; hiring new staff to assure the accuracy of clinical documentation and POA oversight structures; increased time burden for physicians, nurses, and coders; need to upgrade or purchase new software; and need to collaborate with hospital departments or staff that did not interface directly in the past. The policy was adopted by a majority of other payers, although the list of conditions and payment penalties varies. The HAC-POA policy is invisible to patients; therefore, the presence or lack of unintended consequences to patients cannot be fully assessed at this time. Understanding of policy effects to all stakeholders is important for maximizing its successful implementation and desired impact.
OBJECTIVES Medicaid pays for about half the births in the United States, at very high cost. Compared to usual obstetrical care, care by midwives at a birth center could reduce costs to the Medicaid program. This study draws on information from a previous study of the outcomes of birth center care to determine whether such care reduces Medicaid costs for low income women. METHODS The study uses results from a study of maternal and infant outcomes at the Family Health and Birth Center in Washington, D.C. Costs to Medicaid are derived from birth center data and from other national sources of the cost of obstetrical care. RESULTS We estimate that birth center care could save an average of $1,163 per birth (2008 constant dollars), or $11.6 million per 10,000 births per year. CONCLUSIONS Medicaid is the leading payer for maternity services. As Medicaid faces continuing cost increases and budget constraints, policy makers should consider a larger role for midwives and birth centers in maternity care for low-risk Medicaid pregnant women.
OBJECTIVE:This work provides descriptive statistics on hospice users. It also explores the magnitude of relative resource use during hospice episodes and whether such patterns vary by episode length for patients who only use routine home care as compared to those who use multiple levels of hospice care. Examining resource use for hospice users who require different hospice levels of care within an episode versus solely routine home care provides insight to the varied resource use associated with the different patient populations (i.e., those who may require steady routine home care across the entire episode versus those who require varied levels of care across the episode).DATA SOURCE:The analyses were based on a longitudinal analytic file that was constructed from 100% of Medicare claims for hospice users with completed episodes spanning September 1, 2008 through the end of calendar year 2011. In examining resource use for routine home care users and all levels of hospice care, the analyses were restricted to single episode decedents who began their hospice episode on or after April 1, 2010 and whose date of death was on or before December 31, 2011. Daily wage-weighted visit units (WWVUs) were calculated for each patient during their hospice stay. In order to compute a WWVU, one-fourth of the Bureau of Labor Statistics hourly wage rate for each visit discipline (i.e., skilled nursing, medical social services, home health aide, and an average for therapies) was multiplied by the corresponding number of visit units reported on hospice claims.PRINCIPAL FINDINGS:Using enhanced data on the intensity of service use, the results confirm previous research that suggested a curved pattern to service use during a hospice episode. For several measures of resource intensity, service use is more intensive during the initial days in the episode and for the last few days prior to death relative to the middle days of the episode. The pattern becomes more pronounced as episodes increase in length, but is otherwise a similar curve when compared by diagnosis. Thus, the results provide useful information for potential policy discussions about Medicare hospice reform.
OBJECTIVE:Descriptive analysis comparing changes in hospital inpatient readmissions to emergency department visits and observation stays that occurred within 30 days of an inpatient stay.POPULATION:Medicare fee-for-service (FFS) beneficiaries that had at least one acute hospital inpatient stay.DATA SOURCE:Using 100 percent of claims in the Chronic Condition Data Warehouse, we compare growth in annual readmission stays to post-hospitalization emergency department visits and observation stays that were not accompanied by an inpatient stay. Comparisons are performed at the national level and within the Dartmouth Hospital Referral Regions (HRRs).RESULTS:In calendar year 2012, the national, all-cause, 30-day hospital readmission rate among Medicare FFS beneficiaries was 18.5 percent, a significant decline from 19 percent in 2011, which was also the average rate over the previous five years. The number of index admission stays per-1,000 Medicare beneficiaries declined by 4.3 percent, from 283.4 in 2011 to 271.3 in 2012. On a per-1,000 beneficiary basis, the number of readmission stays declined by 6.8 percent, from 53.8 in 2011 to 50.1 in 2012. On the same per-beneficiary basis, the rate of outpatient visits to an emergency department occurring within 30 days of an index hospitalization remained similar at 23.5 in 2011 and 23.4 in 2012. Per-1,000 beneficiaries, the number of observation stays within 30 days of an index hospitalization increased by 0.3 percent, from 3.4 in 2011 to 3.7 in 2012.DISCUSSION:The reasons behind the decline in the Medicare readmission rate in 2012 are not yet clear. When looking at utilization changes in absolute terms, our findings suggest that the reduction in the nation-wide readmission rate observed in 2012 was not primarily the result of increases in either post-index ED visits or post-index observation stays.
OBJECTIVE:The Medicare Current Beneficiary Survey's (MCBS) Access to Care (ATC) file is designed to provide timely access to information on the Medicare population, yet because of the survey's complex sampling design and expedited processing it is difficult to use the file to make both "always-enrolled" and "ever-enrolled" estimates on the Medicare population. In this study, we describe the ATC file and sample design, and we evaluate and review various alternatives for producing "ever-enrolled" estimates.METHODS:We created "ever enrolled" estimates for key variables in the MCBS using three separate approaches. We tested differences between the alternative approaches for statistical significance and show the relative magnitude of difference between approaches.RESULTS:Even when estimates derived from the different approaches were statistically different, the magnitude of the difference was often sufficiently small so as to result in little practical difference among the alternate approaches. However, when considering more than just the estimation method, there are advantages to using certain approaches over others.CONCLUSION:There are several plausible approaches to achieving "ever-enrolled" estimates in the MCBS ATC file; however, the most straightforward approach appears to be implementation and usage of a new set of "ever-enrolled" weights for this file.
OBJECTIVE:To assess the availability, completeness, and quality of the Behavioral Health Organization (BHO) encounter data in MAX 2009.DATA SOURCE:The Medicaid Analytic Extract (MAX) 2009.METHODS:We compared metrics of reporting completeness and quality for BHOs to similar metrics for six states that primarily cover MH and SA services on a FFS basis. For the IP file, number of encounters per 1,000 person months of enrollment were compared. In the OT file, we examined three completeness measures: the number of claims per PME, number of claims reported per BHO outpatient service user, and the number of OT claims per service user.PRINCIPAL FINDINGS:Out of the 15 states reporting enrollment in BHO plans in MAX 2009, 10 reported complete capitation data. IP encounter data were available in four states (Arizona, Colorado, Florida, and Iowa), compared well to FFS ranges, and appear usable for research. OT data are available for five states, but our analysis suggests data are only sufficiently complete for analysis in Arizona and Iowa.CONCLUSIONS:The initial assessment of the availability, completeness and quality of BHO encounter data in MAX 2009 suggests that only limited data are available and usable.
OBJECTIVEExamine the factors that are associated with awareness of physician quality information (PQI) among older people with one or more chronic illnesses and the implications for Medicare.DATA SOURCES/STUDY SETTINGRandom digit-dial survey of adults with one or more chronic illnesses.RESEARCH DESIGNStructural equation modeling to examine factors related to awareness of PQI.RESULTSAwareness of PQI is low (13 percent), but comparable to findings in general population surveys. Age, race, education, and self-reported health status are associated with PQI awareness. Trust in the Internet as a source of health care information and not trusting one's physician as a source of information both are associated with a greater likelihood of being aware of PQI. Patients with high levels of activation have greater trust in physicians as information sources, but this is not associated with awareness, nor is degree of satisfaction with their care experience.CONCLUSIONSAwareness of PQI among older persons with chronic illnesses is relatively low across all socio-economic and demographic subgroups. Changes in population characteristics over time are unlikely to improve awareness in this population, nor are changes in patient activation or satisfaction with care. Medicare would need a broad-based effort if it wishes to raise PQI awareness among Medicare beneficiaries in the near term. Before undertaking resource-intensive efforts to increase awareness, Medicare may want to consider what level of awareness actually is needed to accomplish the overall objective for PQI transparency, which is raising the quality of care received by beneficiaries. It may be that relatively low levels of awareness are sufficient.
