David A. Ganz, MD, PhD

 Poor coordination across care transitions for patients with chronic ambulatory care–sensitive conditions (ACSCs) leads to adverse clinical outcomes. Veterans are at high risk for post–emergency department (ED) adverse outcomes, but the care needs of patients leaving the ED after “treat-and-release” visits are poorly characterized. To inform intervention development and implementation, we assessed for medication changes and follow-up care needs among patients with treat-and-release Veterans Affairs (VA) ED visits for chronic ACSCs.

 We identified treat-and-release ED visits at the Greater Los Angeles VA (10/1/2017-6/30/2018) with diagnostic codes (in any position) for the ACSCs of asthma, chronic obstructive pulmonary disease, heart failure, diabetes, and/or hypertension. For 249 randomly selected visits, a trained nurse abstractor reviewed the ED notes using a structured abstraction tool.

Most of the patients (91%) were male; the median (IQR) age was 67 (58-73) years. In 128 (51%) visits, a medication change was recommended; a new medication was prescribed in 109 (44%), changed in 24 (10%), and stopped in 7 (3%) visits. One or more follow-up care needs were specified in 165 (66%) visits; 55 (22%) specified 2 needs, and 13 (5%) specified 3 or more needs. The 2 most common follow-up care needs were symptom check (41%) and potential medication adjustments post ED (21%).

 More than half of patients with treat-and-release ED visits for chronic ACSCs have recommended medication changes, and two-thirds have at least 1 follow-up care need. This information offers potential foci for testing interventions to improve care coordination for patients with ACSCs who are released from the ED.

To inform intervention development, we assessed for medication changes and patient care needs following treat-and-release Veterans Affairs (VA) emergency department (ED) visits for chronic ambulatory care–sensitive conditions (ACSCs).

More than half of patients discharged home from VA EDs with chronic ACSCs (asthma, chronic obstructive pulmonary disease, heart failure, diabetes, hypertension) have been directed to start, stop, or change the dose of their medications; two-thirds have recommendations for follow-up care (eg, symptom or sign reassessment, laboratory tests).

Most, although not all, of these post-ED care needs can be met by primary care, and many could be met through virtual care mechanisms.

This information is critical for guiding design and implementation of infrastructure and linkages for ED follow-up care coordination interventions that optimize patient safety and outcomes in the VA and other integrated health care systems.

Poor coordination across care transitions leads to adverse clinical outcomes, inefficient care, and less favorable patient experiences.

 The treat-and-release emergency department (ED) visit, in which a patient is discharged from the ED to home or to a skilled nursing facility, is an example of a transition vulnerable to poor outcomes. Across the United States, there are approximately 140 million ED visits each year, 80% of which are treat-and-release visits; of these, 88% have follow-up care recommendations or referrals.

 Patients who do not receive follow-up care or have unmet needs after ED visits are at higher risk for unfavorable clinical outcomes, including ED revisits and hospitalizations.

Despite this risk for poor outcomes, there has been relative underinvestment in both research and implementation of practices to support communication and care coordination for treat-and-release ED visits. In 2017, a National Quality Forum panel assessed the current state of the science on this issue and concluded that investment is needed in developing new infrastructure and linkages to support communications and coordination following treat-and-release ED visits.

 The report further concludes that research is needed for understanding which patients are at highest risk for encountering problems across this transition.

Veterans, who on average are older and have more complex medical histories than nonveterans, are at particularly high risk for post-ED adverse outcomes

 and thus are an important patient population to focus on when assessing the post-ED transition. These risks may be even higher for veterans presenting to the ED with chronic ambulatory care–sensitive conditions (ACSCs), which include asthma, chronic obstructive pulmonary disease (COPD), heart failure, diabetes, and hypertension. Timely outpatient care has been shown to prevent adverse outcomes among patients with chronic ACSCs.

 In addition, management of chronic ACSCs is dependent on medications, and medication changes across transitions are a well-documented source of medical errors.

Therefore, in patients with treat-and-release ED visits for chronic ACSCs, it is important to have clear understanding of the prevalence and types of ED medication changes and of post-ED care needs they may have—both to characterize these patients’ level of risk and to inform efforts to design and build infrastructure and linkages that improve communication and care coordination, and thereby patient safety and outcomes, after ED visits for all patients. Thus, we assessed for medication changes and follow-up care needs among patients with treat-and-release Veterans Affairs (VA) ED visits for chronic ACSCs.

Using administrative data from VA’s Corporate Data Warehouse, we identified 684 treat-and-release visits to the VA Greater Los Angeles (GLA) ED between October 1, 2017, and June 30, 2018, with an 

International Classification of Diseases, Tenth Revision

) code (in any diagnostic position) indicating a chronic ACSC (J44 [COPD], J45 [asthma], I50 [heart failure], E08-E13 [diabetes], I10-I11 [hypertension]). Of these, 300 visits were randomly selected, and for each, a trained registered nurse abstractor (K.A.) reviewed all ED physician and nurse notes, including written discharge instructions. A physician researcher (K.M.C.) reviewed the notes and abstracted information for the initial 50 visits to ensure concordance, providing feedback and additional training as needed, and then answered clarifying questions as they arose in the course of abstracting the remaining 250 visits. For visits that were confirmed to be in person and for an ACSC, we abstracted information about changes to patients’ medications and ED provider recommendations for specific follow-up care using a structured electronic tool with branching logic; this was created using Research Electronic Data Capture (REDCap).

 The tool prompted the abstractor to capture all ED provider recommendations for starting, stopping, or changing the dose or frequency of a medication. For follow-up care, the abstractor was prompted to capture recommendations for post-ED symptom checks; laboratory, radiology, or other tests; blood pressure or weight measurements; potential medication adjustments; follow-up appointments with primary care or medical, surgical, or mental health specialists; and other follow-up care needs. We tabulated medication and follow-up care recommendations, across and within each chronic ACSC condition, using Stata version 15 (StataCorp). This work was determined to be nonresearch by the GLA Institutional Review Board.

Over 9 months, there were 684 treat-and-release ED visits assigned a chronic ACSC 

 code. Of 300 randomly selected visits, we excluded 3 (1%) that were by telephone only and 48 (16%) for which the ED notes did not indicate that the visit included care for a chronic ACSC (ie, the 

 code appeared to be erroneous). The remaining 249 visits were made by 222 unique patients; 12 patients had 2 included visits, and 4 patients had 3 or more. Patients had a median (IQR) age of 67 (58-73) years; 203 (91%) were men. Across visits, 40 (16%) were for asthma; 73 (29%), COPD; 45 (18%), diabetes; 17 (7%), heart failure; and 88 (35%), hypertension. (Percentages add to 105% because in 14 [6%] visits, care was provided for 2 ACSCs.) In 97 (39%) visits, care for a non-ACSC was also provided.

, in 128 (51%) of ED visits, a medication change was recommended, with 108 (44%) visits having 1 or more new medications started, 24 (10%) having a medication dose or frequency changed, and 7 (3%) having a medication stopped. Medication changes were most common in visits for COPD, with 68 (93%) of visits resulting in a recommendation for a medication to be started, changed, or stopped. More than half of visits for asthma (n = 22; 55%) and heart failure (n = 9; 53%), and more than a quarter of visits for diabetes (n = 13; 28%) and hypertension (n = 23; 26%), had a recommended medication change.

With respect to follow-up care recommendations, shown in 

, 165 (66%) of the chronic ACSC visits had 1 or more follow-up care needs; 55 (22%) visits specified 2 needs and 13 (5%) specified 3 or more needs. The 2 most common follow-up care needs were symptom checks (n = 101; 41%) and post-ED potential medication adjustments (n = 53; 21%). Other follow-up care needs included blood pressure measurements (n = 33; 13%), medical/surgical specialty care (n = 17; 7%), laboratory tests or results (n = 12; 5%), social work services (n = 10; 4%), radiology tests (n = 7; 3%), specialized testing (eg, heart, pulmonary, or sleep studies) (n = 6; 2%), mental health care (n = 4; 2%), nutrition counseling (n = 2; 1%), and weight measurement (n = 1; 1%). Details on the types of medical/surgical specialty care, laboratory tests or results, radiology tests, and specialized testing follow-up care needs are shown in the 

). Most social work services (captured in the “other” follow-up recommendation category on the REDCap form) centered around housing support services. Heart failure visits had the highest likelihood of having 1 or more follow-up care needs (71%), followed closely by COPD (70%), hypertension (68%), diabetes (67%), and asthma (58%) visits.

Among patients with treat-and-release VA ED visits for chronic ACSCs, more than half have recommended medication changes and two-thirds have primary care, specialty care, and/or social work follow-up care needs. Not uncommonly, patients have multiple follow-up care needs after chronic ACSC ED visits. These findings demonstrate the extent to which patients with ED visits for chronic ACSCs are at high risk for encountering problems across the post-ED care transition.

The high prevalence of ED provider recommendations for medication changes points to an area of particular vulnerability for miscommunication and potential safety concerns. These medication changes need to be effectively communicated to patients, their caregivers, and their primary and specialty care providers. Infrastructure is needed to support all medication changes being clearly specified in transition records and in any other forms of asynchronous communications between ED and follow-up care providers.

 Further, although studies have shown that medication errors and unintentional discrepancies are common following hospital discharge,

 with determinants spanning both patient and system factors,

 similar research is needed to assess the prevalence, as well as determinants and mitigators, of these problems following ED visits.

In addition, the high prevalence of medication changes suggests the potential value of integrating clinical pharmacy services within EDs to support optimal medication reconciliation, prescribing, and patient medication education.

 Communication issues between providers and patients about medications are a source of avoidable adverse events. Studies show that pharmacy-led medication reconciliation in the ED decreases medication discrepancies and potential adverse drug events.

 Pharmacist involvement in transitions for complex patients decreases posthospital readmissions and ED visits.

 The extent to which pharmacist involvement in the ED could similarly prevent ED revisits, as well as post-ED medication errors and unintentional discrepancies, for patients with chronic ACSCs is a topic for potential future research.

With respect to our finding of the high prevalence of post-ED care needs among patients with chronic ACSCs, clear and complete information about these needs is key for lessening patient anxiety and uncertainty; decreasing risk of adverse outcomes, including ED revisits, hospitalizations, and death; and optimizing use of health care resources.

 Patient-centered medical homes, including VA’s Patient Aligned Care Teams (PACTs), are designed to facilitate care coordination and communication across transitions

 and should play an integral role in interventions to improve receipt of needed post-ED follow-up care. However, although primary care can meet most of the needs detailed in our findings, some needs, such as specialty care and specialized testing, will necessitate coordinating with other services. Of note, many of these needs could potentially be met virtually (eg, a telephone/video visit for symptom checks, the most prevalent need).

The VA has initiated developing and implementing evidence-based interventions to ensure that patients get the care they need following VA ED visits.

 The ED-PACT Tool facilitates communication between ED providers and PACT staff, using messages sent via the VA electronic health record.

 Separately, the ED-Rapid Access Clinics initiative enables VA EDs to directly schedule appointments for specialty follow-up care

; multiple studies have shown that direct scheduling improves the receipt of post-ED care.

 For instance, the results of a research study by Hastings and colleagues—the VA Discharge Information and Support for Patients receiving Outpatient care in the Emergency Department—showed that among high-risk veterans with chronic health conditions, post-ED structured telephone support can increase the rate of having at least 1 primary care visit within 30 days of a VA ED visit.

 Further work is needed to investigate the effects of these innovations on patient experience and outcomes, assess their implementation, promote their spread across VA, and consider how they might be adapted for veterans using non-VA EDs.

 Similar interventions are also needed within integrated care systems beyond VA.

The most salient limitation of this work is that it was performed in a single VA facility. Future work should assess for similarities and differences in patients’ ACSC follow-up care needs across VA EDs and assess transitions after treat-and-release ED visits in other systems of care. Descriptions of follow-up care needs in patients presenting to VA EDs with other conditions, and to non-VA EDs, are also needed. An additional limitation in our methods is that although a second individual reviewed the initial 50 visits to ensure abstraction consistency and accuracy, we did not utilize a second reviewer in a way that allows calculation of interrater reliability. Finally, we included visits with ACSCs that had 

 codes in any diagnostic position; ACSC codes not in the primary position indicate that the ACSC was not the main focus of care. Discharge medications and follow-up care needs may differ between those who have an ACSC as a primary vs a nonprimary diagnosis.

The high prevalence of medication changes and follow-up care needs among patients with treat-and-release VA ED visits for chronic ACSCs suggests a pressing need for communication and care coordination following these visits. Further work is needed to ascertain the status and functioning of post-ED communication and coordination mechanisms across VA, with assessments of whether patients are receiving needed care, the nature of their experiences in obtaining that care, potential variations in care receipt by condition, and need for patient medication reconciliation and education support during and following ED visits. Such work is critical for optimizing patient safety and post-ED outcomes in the VA and in other integrated health care systems.

The authors would like to acknowledge the administrative assistance of Gracielle Tan, MD, health science specialist at the VA Los Angeles Health Services Research and Development Center for the Study of Healthcare Innovation, Implementation, and Policy (CSHIIP), and editorial review and feedback of Chloe Bird, PhD, senior sociologist, RAND Corporation, Santa Monica, California. Dr Bird’s time was supported through CSHIIP (project #CIN 13-417).

 VA Center for the Study of Healthcare Innovation, Implementation, and Policy (KMC, AHY, DAG), Los Angeles, CA; VA Greater Los Angeles Healthcare System (KMC, DAG), Los Angeles, CA; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles (KMC, DAG), Los Angeles, CA; Los Angeles Department of Public Health (KA), Los Angeles, CA; RAND Health (DAG), Santa Monica, CA.

 Support for this work was provided by the Department of Veterans Affairs (VA), VA Quality Enhancement Research Initiative (QUERI), Care Coordination QUERI, project #QUE 15-276. The views expressed within are solely those of the authors and do not necessarily represent the views of the Department of Veterans Affairs or the United States government.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (KMC, DAG); acquisition of data (KMC, AHY, KA); analysis and interpretation of data (KMC); drafting of the manuscript (KMC); critical revision of the manuscript for important intellectual content (KMC, AHY, KA, DAG); statistical analysis (KMC); provision of patients or study materials (KMC); obtaining funding (KMC, DAG); administrative, technical, or logistic support (KMC, AHY, KA); and supervision (KMC).

 Kristina M. Cordasco, MD, MPH, MSHS, VA Center for the Study of Health Care Innovation, Implementation, and Policy, 11301 Wilshire Blvd, Bldg 206, 2nd Floor, Los Angeles, CA 90073. Email: Kristina.Cordasco@va.gov.

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Articles

To inform intervention development, we assessed for medication changes and patient care needs following treat-and-release Veterans Affairs emergency department visits for chronic ambulatory care–sensitive conditions.

Patients’ Needs Following Emergency Care for Ambulatory Care–Sensitive Conditions

 To compare home health utilization and clinical outcomes between Medicare beneficiaries in the fee-for-service (FFS) and Medicare Advantage (MA) programs, and to compare regional variation.

 We used the 2010 and 2011 Outcome and Assessment Information Set to identify all home health episodes begun in 2010 and to measure 7 clinical home health outcomes that are defined by CMS for public reporting.

 We modeled the probability of home health use, the duration of home health episodes, and each clinical outcome measure as a function of MA versus FFS enrollment and model-specific risk adjustors. Empirical Bayes predictions from generalized linear mixed models were aggregated by hospital referral region (HRR) to create standardized regional measures of home health utilization and mean episode duration

We identified 30,837,130 FFS and 10,594,658 MA beneficiaries (excluding those dually eligible for Medicaid). After adjusting for demographic and clinical patient characteristics, the odds of receiving home health among FFS enrollees were 1.83 times those of MA (95% CI, 1.82-1.84). Adjusted home health duration was 34% longer for FFS (95% CI, 32%-34%). Outcomes differences were small in magnitude and inconsistent across measures. Regional variations in use and duration were substantial for both FFS and MA enrollees. Within HRRs, correlations between FFS and MA utilization rates and between FFS and MA episode durations were 0.51 and 0.94, respectively.

 MA beneficiaries use less home health than their FFS counterparts, but regional factors affect utilization, independent of insurance status.

Home health utilization is a substantial source of Medicare costs; however, whether enrollment in a Medicare Advantage (MA) plan affects home heathcare use is unknown. We compared nationwide and intra-regional use of home health between beneficiaries enrolled in fee-for-service (FFS) Medicare and MA plans. Our findings were as follows:

		FFS beneficiaries used substantially more care, even after adjusting for patient and demographic characteristics.

		Functional outcomes were similar, although FFS episodes were 33% longer in duration.

		Adjusted utilization varied markedly across regions for both FFS and MA, suggesting that MA plans cannot entirely overcome non—health-related regional forces driving use.

Although more than 30% of all Medicare beneficiaries are enrolled in the Medicare Advantage (MA) program,

 studies focusing on the MA patient population are relatively rare, in large part because there are no claims to use as a data source. The capitated payment model introduces a financial incentive to limit wasteful care, whereas none exists in the fee-for-service (FFS) system. However, few published studies have directly measured and compared how much a particular service is used in the 2 systems.

One venue where care is documented for all Medicare beneficiaries is home health. All patients receiving care through CMS-certified home health agencies are required to have a comprehensive clinical assessment at the start and end of care, using the Outcome and Assessment Information Set (OASIS) instrument. For both FFS and MA beneficiaries, there is therefore a record not only that care occurred, but also of the clinical status at the start of care and of the functional outcome. Thus, home health provides a unique window through which the 2 programs can be compared.

The use of home health within Medicare is a topic ripe for review. Home health accounted for more than 5% of all FFS Medicare spending ($18.4 billion) in 2011,

 and an Institute of Medicine (IOM) report identified postacute care as the leading source of unexplained regional variation in healthcare spending via FFS.

 In a report to Congress, the Medicare Payment Advisory Commission (MedPAC) suggested that much of the variation in home health use reflected “fraud, waste, and abuse,” and noted that “the broad program standards and fragmented nature of the FFS program do not encourage effective targeting of the benefit.”

 Whether MA plans, with their incentives to avoid unnecessary care, are more effective at managing home health use is a natural next question. As a first step toward answering it, we compared home health utilization, regional variation, and clinical outcomes among beneficiaries enrolled in the MA and FFS programs.

We identified all home health episodes among Medicare beneficiaries that started in 2010 and ended by December 31, 2011. We first performed a beneficiary-level analysis, comparing the probability (after adjusting for patient characteristics) that an FFS versus an MA beneficiary would have at least 1 home health episode. For those who received home health, we also compared the total number of days enrolled and 7 clinical outcomes that are publicly reported by CMS on the “Home Health Compare” website: improvement in the 3 measures of activities of daily living; acute care hospitalizations; and improvements in pain, dyspnea, and management of oral medications.

 Each of these measures is risk-adjusted (per CMS specifications) using the results of the OASIS assessment at the start of care.

 With the exception of the hospital utilization measure, outcomes are defined by comparing end-of-care and start-of-care OASIS assessments, and are defined only for patients who remain living in the community at the end of the episode.

We first compared each study endpoint as adjusted averages across the FFS and MA populations. Then, to better understand patterns of utilization differences between FFS and MA, we compared FFS and MA beneficiaries within each of the 306 hospital referral regions (HRRs). HRRs were chosen as the geographic unit of interest because the system partitions the country into mutually exclusive regional healthcare markets, which were felt to be the best available approximation for regions serviced by home health agencies.

Using the 100% OASIS file, we grouped start-of-care and end-of-care assessments into episodes according to CMS specifications.

 OASIS episodes represent a complete cycle of care that ends only when a patient is hospitalized, dies, or is discharged to the community. An OASIS episode may encompass 1 or more 60-day billing episodes, as defined by the home health prospective payment system.

 To each OASIS episode (observation), we merged in data from the  100% denominator file and from a file containing Hierarchical Condition Category (HCC) scores (which represent the ratio of an individual’s expected cost to that of an average beneficiary).

The Medicare health insurance claim number or the combination of Social Security number and date of birth were used as unique identifiers.  We also added demographic data from the American Community Survey, using 5-year estimates for 2007 to 2011, by linking via the zip code of each beneficiary.

Because patients who were dually eligible for Medicare and Medicaid may have received home health benefits through Medicaid—which would not have been visible to us&mdash;we excluded dual-eligible beneficiaries from the analysis. All others were included.

We used logistic regression with robust standard errors to model the odds of receiving home health and the odds of each of the 7 clinical home health outcomes. We used a generalized linear model with a log link and Gaussian distribution to model the geometric mean number of days enrolled in home health (for those who received care). For models of utilization, risk adjustors included age, sex, HCC score, and 2 socioeconomic indicators from the 2007 to 2011 American Community Survey: deciles of median household income in the Zip Code Tabulation Area (ZCTA) and the proportion of households in the ZCTA with only 1 resident (among those with 1 or more residents aged ≥65), as surrogates for the beneficiary’s income and the probability that they live alone. For home health duration, we used 2 additional risk adjustors: whether the initial home health episode started after a hospitalization (postacute) and whether it followed a surgical procedure. For each clinical outcome, we used an extensive set of outcome-specific risk adjustors (derived from the baseline OASIS assessment) that is specified by CMS for the Home Health Compare program.

To generate region-specific standardized utilization rates, we used generalized linear mixed models (with logit link for the probability of receiving home health and log link for home health duration) to model home health use as described for the national analysis, but added a random intercept for HRR. We then aggregated recycled empirical Bayes predictions to create a standardized utilization rate (or mean duration) for MA and FFS, for each of the 306 HRRs.

 We presented our results graphically by plotting each region’s FFS utilization rate against the MA utilization rate, and reported the correlation coefficients for MA versus FFS across the 306 HRRs.

In keeping with recent reports by MedPAC and the IOM, we quantified regional variation by reporting the ratio of the 90th percentile versus the 10th percentile of regional utilization.

 We reported these ratios separately for the FFS and MA populations.

After excluding 8,254,838 individuals dually eligible for Medicare and Medicaid, we identified 30,837,130 FFS beneficiaries and 10,594,658 MA beneficiaries. Baseline characteristics and unadjusted home health utilization rates are summarized in 

. Among the general population, MA patients were slightly older, whereas in the population using home health, the opposite was true. HCC scores were higher for MA in both populations.

Of those enrolled in FFS, 6.3% had at least 1 home health episode; of those in MA, 3.9% did. Among FFS beneficiaries who used home health, the average number of enrolled days was 75.8; among MA beneficiaries, it was 56.3 days. Although MA patients using home health had higher HCC scores than their FFS counterparts (suggesting a higher disease burden), they had less baseline disability according to each of 6 different measures at the start-of-care OASIS assessment. Home health more commonly started after a hospitalization and/or a surgical procedure for MA patients.

FFS Versus MA: Adjusted National Estimates

. We estimated that among the general Medicare population, the odds of starting home health during 2010 were 1.82-fold higher for those enrolled in FFS versus MA, after adjusting for patient characteristics and socioeconomic indicators. Among those who did start home health in 2010, we estimated that the FFS patients were enrolled an average of 34% more days than their MA counterparts, after risk adjustment.

The relationship between FFS versus MA, in regard to clinical home health outcomes, was inconsistent, and observed differences were small in magnitude: FFS outcomes were better with regard to pain (odds ratio [OR], 1.08; 95% CI, 1.07-1.09), ambulation (OR, 1.01; 95% CI, 1.00-1.02; 

 = .03), and management of medications (OR, 1.06;  95% CI, 1.04-1.07); and MA outcomes were better with regard to bed transfer (OR, 0.98;  95% CI, 0.97-0.99), dyspnea (OR, 0.99;  95% CI, 0.98-1.00; 

 = .02), and hospital utilization (an adverse outcome) (OR, 1.16; 95% CI, 1.15-1.17). Improved bathing was not significantly related to FFS versus MA enrollment (OR, 1.00; 95% CI, 0.98-1.02). Hospital utilization, which measures whether home health ended in hospitalization rather than discharge to the community, was the outcome with the greatest difference (favoring MA). Of note, this outcome is not adjusted for duration of home health. Because FFS patients—who have longer home health episodes&mdash;spend more time at risk for hospitalization while enrolled, this relationship may be confounded.

Among the 10% of HRRs (ie, 31 of 306 HRRs) with the highest proportions of FFS beneficiaries with 1 or more home health episodes (90th-percentile FFS), we estimated that 9.4% of all FFS beneficiaries used home health, whereas among the 31 HRRs with the lowest proportion (10th-percentile FFS), only 4.2% did. The ratio of the 90th percentile/10th percentile for FFS was, therefore, 2.2. For MA patients, the 90th percentile for use of home health was 6.3%, the 10th percentile was 2.2%, and the ratio was 2.8. The higher 90th/10th percentile ratio for MA means that by this measure, regional variation in home health use was higher for MA patients than for FFS patients.

An analogous evaluation of the duration of home health episodes yielded the following results: in the 90th-percentile HRR (with regard to average episode duration), mean duration was estimated to be 92 days for FFS and 67 days for MA. The 10th percentile was 39 days for FFS and 31 days for MA. The ratios of 90th/10th percentiles were 2.4 for FFS and 2.2 for MA.

 shows the standardized proportions of FFS and MA beneficiaries receiving home health in each of the 306 HRRs. 

 shows the analogous mean (standardized) duration of home health episodes. These figures illustrate the correlation between FFS and MA rates of home health use (Figure 1), and average home health duration (Figure 2) across regions. Correlation coefficients for MA versus FFS across regions were 0.51 for the proportion using home health and 0.94 for mean duration, meaning regions with longer home health episodes among MA patients also tend to have longer episodes among FFS patients and that this relationship exists to a lesser extent with regard to the probability that an MA or FFS beneficiary in a given region will use home health.

We have shown that Medicare beneficiaries enrolled in FFS use substantially more home health than those in MA, both in terms of the proportion of beneficiaries who receive care and the duration of home health among those who do. This is true after adjusting for age, disease burden, and regional differences in household size and income. We found that despite the average home health episode duration being 34% longer for FFS, differences in clinical outcomes were small in magnitude and inconsistent. Of the 7 outcomes that we measured, FFS fared slightly better on 3, MA fared slightly better on 3 others, and there was no difference on 1.

Assertions of “fraud, waste, and abuse” in the home health industry are based on data from the FFS program and have been made in the context of a history of rapid fluctuations in home health use that have closely paralleled changes in home health agency reimbursement policy.

 In 1997, after Medicare spending for home health quadrupled over a 5-year period, cost-based reimbursement was abruptly replaced with a forerunner of today’s home health prospective payment system, whereupon spending and utilization rates were cut in half within a year. Subsequently, utilization and expenditures have again crept upward, and since 2002, total FFS home health expenditures have nearly doubled.

The MA versus FFS comparison of home health utilization across regions is of interest because MA plans, which are paid a capitated rate to cover all needed services, have a direct incentive to minimize financial costs, whereas there is no real equivalent incentive for FFS. It is unclear how financial incentives might affect an MA plan’s “propensity to provide home health.” On the one hand, MA plans might be expected to minimize the direct costs of paying home health agencies and restrict the use of home health. On the other, they might substitute home health for more costly inpatient services and be more inclined to promote its use. Whichever the predominant effect, whether MA enrollees actually use less home health than their FFS counterparts also depends on the proportion of FFS home health that is truly wasteful and the practical ability of MA plans to curb such waste. Small studies from the pre-prospective payment system era suggested that Medicare health maintenance organizations (the forerunner of today’s MA plans) used substantially less home health than their FFS counterparts at the time, and our study confirms that this is still true (albeit to a lesser degree).

Regional variation is of interest because it provides insight into the practical limitations to optimizing the use of a service: if MA plans were perfectly able to optimize health outcomes or their own financial interests, then unexplained regional variation (ie, beyond what can be explained by differences between patients) would not be expected. The strong correlation between the use of home health by MA and FFS patients within regions therefore implies that MA plans’ ability to manage utilization is limited; MA plans limit use, but cannot entirely overcome the same regional forces (eg, healthcare market, local practice styles) that drive variation across regions in the FFS population.

We found a greater correlation between the duration of home health episodes for MA versus FFS patients within a region compared with the regional correlation in the proportion of beneficiaries enrolled in MA versus FFS who receive any home health at all. One explanation might be that home health agencies have more leverage in determining duration of care than they do in determining whether beneficiaries are referred to them. If most home health agencies treat a mix of MA and FFS beneficiaries, and if they provide care in a “payer-blind” way, then this finding might be expected.

There has been little previous study of regional variation in the use of a service by MA beneficiaries. One study did compare the use of cardiovascular procedures across 32 of 306 HRRs and found regional FFS and MA rates to be correlated, albeit less so than we find here.

 An IOM report has also noted regional correlation (0.66) in total per-capita spending.

 Regional variation among the FFS population has been extensively studied, but we found more variation in FFS home health use than has been recently described for most other services or for postacute care in general.

 We noted that postacute care encompasses more than home health and that not all home health is post acute. However, a 1996 study of FFS home health use during an era prior to the prospective payment reform found a degree of regional variation that appears to be on par with what we find here (methodological differences preclude an exact comparison).

We cannot directly determine whether greater home health use by FFS beneficiaries means that they receive unnecessary care or whether MA beneficiaries are not receiving necessary care; it is likely that both are sometimes true (as would sometimes be the converse). Measurement limitations notwithstanding, our finding that MA and FFS patients have similar clinical outcomes suggests that the shorter MA episodes may not be detrimental to health. We do not address patient preferences, however; nor do we explore the clinical effect of differing rates of home health use (compared with differing episode durations).

Our study has several other limitations. First, because MA plans have a financial incentive to select for healthier patients in ways that are not captured by CMS payment models,

 it is possible that unobservable differences between the populations remain after risk adjustment. Although this could account for some of the difference that we see in a beneficiary’s probability of receiving home health, we would expect less influence on our estimates of home health duration or outcomes, which are conditional on receiving home health and which use a larger set of observables (from OASIS) for risk adjustment. Second, functional outcome measures rely on clinical assessments at the start and end of each home health episode. Because each agency’s performance on these measures is publicly reported, there may be an incentive for agencies to underestimate functional status at the start of care or overestimate it at the end of care, biasing the results toward better outcomes, which would have the effect of minimizing any true outcomes differences between the FFS and MA populations. Moreover, since the end-of-care assessment does not take place when patients die or are transferred, outcomes are only measured for patients who are discharged back to the community at the end of home health. Finally, we do not have a direct window onto the actual number of home health visits that a patient receives, and rely instead on the duration of a home health episode as a proxy. We cannot be certain that FFS and MA beneficiaries receive the same number of visits per unit of time enrolled in home health.

MedPAC has reported to Congress that the FFS system is not structured to limit wasteful use of the home health benefit.

 Our study uses MA as a counterexample of care under an alternative reimbursement structure. The Government Accountability Office cites the lack of standards for what constitutes appropriate and necessary care as one of the challenges to curbing excess. Although we do not offer such a standard, we suggest that the type of within-region, between-payer comparison performed here can help to better define a reasonable range of use and to identify high-use and low-use regional outliers (for both FFS and MA). This methodology may be more widely applicable using MA encounter data, which are still being collected.

The authors acknowledge Patty Smith for editorial assistance, Cheryl Damberg and Rodger Madison for assistance with data access, and Emmett Keeler and Jeffrey Wasserman for comments on a previous version of this paper. 

Department of Emergency Medicine (DAW) and David Geffen School of Medicine (NSW, DAG), University of California, Los Angeles, Los Angeles, CA; RAND Corporation (DAW, LM, CMS, MH, NSW, DAG), Santa Monica, CA; University of Michigan Medical School (LM), Ann Arbor, MI; VA Ann Arbor Healthcare System (LM), Ann Arbor, MI; VA Greater Los Angeles Healthcare System (DAG), Los Angeles, CA.

The analyses upon which this article is based were performed under contract HHSM-500-2005-00028I, task order number HHSM-500-T0004, entitled “Evaluation and Development of Outcome Measures for Quality Assessment in MAOs and SNPs,” funded by CMS and HHS. The content of this article does not necessarily reflect the views or policies of HHS, nor does the mention of trade names, commercial products, or organizations imply endorsement by the US government. The author assumes full responsibility for the accuracy and completeness of the ideas presented.

Dr Waxman was supported by the Department of Veterans Affairs (VA) Office of Academic Affiliations through the VA Health Services Research and Development Advanced Fellowship Program; he had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr Ganz was part of the project team on contracts awarded to RAND from CMS and the Health Services Advisory Group, and was part of the project team on a contract awarded to UCLA from SCAN Health Plan. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

Concept and design (DAG, MH, LM, DAW); acquisition of data (DAW); analysis and interpretation of data (DAG, MH, LM, CMS, DAW); drafting of the manuscript (LM, CMS, DAW); critical revision of the manuscript for important intellectual content (DAG, LM, DAW); statistical analysis (CMS, DAW); obtaining funding (DAG, DAW); administrative, technical, or logistic support (MH); and supervision (DAW). 

Daniel A. Waxman, MD, PhD, RAND Corporation, 1776 Main St, Santa Monica, CA 90407. E-mail: dwaxman@rand.org.

1. Medicare Advantage. Kaiser Family Foundation website. http://kff.org/medicare/fact-sheet/medicare-advantage/. Published May 11, 2016. Accessed February 13, 2016.

Medicare Advantage beneficiaries use less home healthcare than do their fee-for-service counterparts, but there is marked regional variation in use by both groups.

Does Medicare Advantage Enrollment Affect Home Healthcare Use?

Although Medicare has implemented incentives for health plans to reduce fall risk, the best way to identify older people at high risk of falling and to use screening results to target fall prevention services remains unknown. We evaluated 4 different strategies using a combination of administrative data and patient-reported information that health plans could easily obtain.

We used data from 1776 patients 75 years or older in 4 community-based primary care practices who screened positive for a fear of falling and/or a history of falls. For these patients, we predicted fall-related injuries in the 24 months after the date of screening using claims/encounter data. After controlling for age and gender, we predicted the number of fall-related injuries by adding Elixhauser comorbidity count, any claim for a fall-related injury during the 12 months prior to screening, and falls screening question responses in a sequential fashion using negative binomial regression models. 

Basic patient characteristics, including age and Elixhauser comorbidity count, were strong predictors of fall-related injury. Among falls screening questions, a positive response to, “Have you fallen 2 or more times in the past year?” was the most predictive of a fall-related injury (incidence rate ratio [IRR], 1.56; 95% CI, 1.25-1.94). Prior claim for a fall-related injury also independently predicted this type of injury (IRR, 1.41; 95% CI, 1.05-1.89). The best model for predicting fall-related injuries combined all of these approaches.

The combination of administrative data and a simple screening item can be used by health plans to target patients at high risk for future fall-related injuries.  

Among a high-risk population of community-dwelling older adults, the best model for predicting future fall-related injuries combined basic administrative data (age, gender, and comorbidity count), a prior claim for a fall-related injury, and patient response to the single item screener, “Have you fallen 2 or more times in the past year?”

			The most important influences on prediction were comorbidity count and response to the screening question. 

			Given current Medicare incentives to reduce fall risk, health plans can use this information to target fall prevention interventions to the highest risk members without requiring a face-to-face office visit.

Reducing fall risk is one of the process measures used to determine the CMS 5-Star Ratings for Medicare Advantage plans, which are published annually, and to determine Medicare Advantage Quality Bonus Payments.

  Moreover, with the passage of the Affordable Care Act, private insurers, Medicare, and Medicaid are likely to be required to provide coverage for new preventive health services graded A or B by the US Preventive Services Task Force (USPSTF),

 including exercise or physical therapy and vitamin D supplementation for fall prevention (USPSTF grade B recommendations).

  However, the best method to screen community-dwelling older adults for fall risk, and the best way to use the screening results to target preventive services for falls, remains unclear.

Several risk factors have been identified for falls among community-dwelling older adults,

 with age and a history of falls being the 2 most commonly used risk factors to define high risk in fall intervention studies.

 Other frequently reported risk factors for falls include a history of mobility problems, poor performance on office-based gait and balance testing such as the Get-Up-And-Go test, visual impairment, use of psychoactive medications, and female gender.

 However, there is no consensus on a best single screening question, set of questions, or clinical tool to reliably identify older adults at increased risk for falls.

Most clinical tools used to assess fall risk are performed in a primary care setting by a clinician,

 requiring a face-to-face office visit, provider training in the use of the screening tool, and provider engagement in the screening process. However, this may not be the preferred approach for a health plan seeking to provide fall prevention services more widely to its at-risk members. Alternative approaches could include screening questions completed over the phone or by mail or the use of health plan administrative data to identify patients with risk factors for falls (eg, age, female gender, certain health conditions) or with a prior claim for a fall or a fall-related injury.

Our objective was to examine the predictive value of different possible screening approaches for fall risk that would be relevant to a health plan seeking to identify its older adult members at highest risk for falls and fall-related injuries. We assessed the value of 3 commonly used falls screening questions in combination with data from Medicare claims in predicting subsequent fall-related injuries in a cohort of high-risk community-dwelling older adults.

We studied 1776 adults 75 years or older who screened positive for falls or fear of falling in 4 community-based primary care practices using 3 questions: 1) Have you fallen 2 or more times in the past 12 months? 2) Have you fallen and hurt yourself since your last visit to the doctor? 3) Are you afraid that you might fall because of balance or walking problems?

Participants were screened during 2006 and 2007 as part of the ACOVEprime study, a controlled trial of a practice-based quality improvement intervention to improve care for falls and urinary incontinence.

This project was approved by the UCLA Institutional Review Board (IRB). The 4 participating sites either approved the project via their own IRB or by deferring to the UCLA IRB. A fifth site received approval to obtain claims only from decedents; thus, data from this site were excluded.

This study evaluates different strategies to identify high fall risk among community-dwelling elders using a combination of easily obtainable administrative data and patient screening questions. 

David A. Ganz, MD, PhD

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