• More from the Commonwealth Fund’s readmissions brief

      09/10/2013
      Austin Frakt

    Yesterday, I quoted the recent Commonwealth Fund Issue Brief on Medicare’s hospital readmission measures, by Clifford Marks, Saranya Loehrer, and Douglas McCarthy. It is based on comments by a panel of experts, all of whom are listed in the brief. Below are the other passages I highlighted as I read. All are direct quotes.

    • Hospitals, academics, and policymakers are heatedly debating the appropriateness of the readmissions metric―even its definition―giving the impression of fundamental disagreement about the program’s value.
    • That the initial policy has flaws is an argument not for abandoning the effort, but for redoubling efforts to improve the measures as well as the incentive system.
    • Patients do not suffer less at 31 days or when their initial diagnosis is diabetes, rather than heart failure. Moreover, the measure fails to capture equally harmful preventable admissions, which many panelists believe should be incorporated into a set of related accountability measures.
    • Measures such as days between hospital encounters or days alive at home permit assessment along a continuum, which may better track what patients desire from health care. Attending to patient needs also requires that readmissions be considered in the context of balancing measures―such as mortality, length of stay, and use of observation status―to help ensure health systems are not eliminating necessary admissions, readmissions, or days in the hospital.
    • A significant criticism of the Medicare readmissions penalty is that hospitals are held financially accountable for certain aspects of care that are beyond their control.

    All prior TIE posts on hospital readmissions are tagged accordingly.

    @afrakt

     
      Health Policy, Research Notebook
      hospital readmissions
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  • Community health workers and hospital readmissions

      09/09/2013
      Austin Frakt

    Regular readers will recall my many posts on the lack of sensitivity of Medicare’s hospital readmissions measures to socioeconomic status. See, for example, this or that. Very attentive readers with an exceptional memory may also recall that I participated in an evaluation of the evidence relating to community health workers (CHWs). Results of that evaluation are here (PDF) and more is linked to from here.

    A few documents relevant to these two threads, and their intersection, crossed my desk in the last week. Below are a few, relevant excerpts. (Emphases added.)

    From Shreya Kangovi, Judith Long, and Ezekiel Emanuel in JAMA:

    Low-income African American patients [] are up to 43% more likely than their higher-income white counterparts to find themselves back in the hospital within weeks of discharge. As a result, the cost of care for these disadvantaged patients is high, as illustrated by the population of low-income patients who are dually eligible for Medicare and Medicaid. Dually eligible individuals cost twice as much as other Medicare beneficiaries largely because they are 4 times as likely to be readmitted to hospitals for ambulatory care–sensitive conditions. […]

    Yet poor health status is only part of the reason for the readmission of patients []. Besides disease burden, factors perpetuate the revolving hospital door for low-income patients: lack of access to medical resources such as a regular source of care, competing socioeconomic issues such as homelessness or food insecurity, and social isolation. […]

    Existing postdischarge interventions frequently fail to help patients []. First, they do not target patients with low socioeconomic status. In fact, many postdischarge services are only available to patients with insurance. Second, even when such services are offered, low-income patients use them at a low rate owing to mistrust of clinical personnel like nurse practitioners and home health aides. Most importantly, many postdischarge interventions are fundamentally clinical interventions, delivered by a workforce trained to address clinical issues. Paradoxically, intensifying clinical follow-up care [] might actually increase admissions; outpatient medical providers often do not have the tools to address the underlying social causes of poor health and have no choice but to refer these patients back to the hospital when they inevitably fall ill. […]

    [T]he CHW workforce, may be able to reach marginalized patients and link them into nonmedical support systems. They can help to address material needs for resources like food or housing as well as social needs, such as the need for purpose or socially meaningful interactions. CHWs share socioeconomic status with the individual patients they serve; this shared life experience affords CHWs a high level of what sociologists call an “empathic understanding” of their patients; they have experienced similar stressors as their patients and have a knowledge of local community resources that clinical staff may lack. Therefore, CHWs may have an enhanced ability to provide both emotional and instrumental support.

    From a NEJM Perspective by Prabhjot Singh and Dave Chokshi:

    The Affordable Care Act (ACA) includes levers to shift our health care system’s focus toward comprehensive, high-quality care for populations. Through structures such as accountable care organizations and incentives such as readmissions penalties, hospitals are increasingly responsible for the care of patients both in and outside the hospital. For example, hospital systems have invested in care coordinators, aiming to reduce readmission rates by stratifying patients according to risk level and tailoring their discharge interventions. As these systems look further beyond their own walls, they may see opportunities for lower-cost, CHW-based programs to demonstrate superior value.

    Beyond reducing readmissions, CHW programs may help to address the root causes of preventable chronic disease. Social exclusion, poverty, marginalization, and the built environment contribute to the high burden of chronic disease, particularly in low-income communities. But social services addressing these social determinants of health are too often fragmented. CHWs who can integrate knowledge of the local social service milieu with knowledge of patients’ individual circumstances can create a vital link for vulnerable populations. In concert with social workers, CHWs can mobilize social support, create avenues for family members to engage in the care process, and strengthen long-term community relationships that help patients sustain healthful behaviors.

    From a Commonwealth Fund Issue Brief that is definitely worth a full read, by Clifford Marks, Saranya Loehrer, and Douglas McCarthy:

    Because the measure used for Medicare’s penalty is not adjusted for patients’ socioeconomic status (SES), and because patients with lower SES experience higher rates of readmissions, safety-net hospitals on average receive higher penalties under the current regime. While adjusting for SES could address this concern, such a move would simply hide and perpetuate a disparity that we as a society should be working to rectify, the panelists noted. […]

    Experts [] noted the futility of discharging vulnerable patients into communities lacking strong networks of primary care and the community support systems necessary to aid patients in their recovery.

    @afrakt

     
      Health Policy
      community health workers, hospital readmissions
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  • Changes in hospital readmission rates by clinical severity

      08/05/2013
      Austin Frakt

    Medicare now penalizes hospitals for high readmission rates for acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia. Anticipating the penalties, readmission rates have come down, though this does not prove a causal relationship.

    medicare readmit trend

    The chart illustrates a highly aggregated metric. Certainly readmission rates and changes in them vary by type of patient (in any number of dimensions). For example, recent work by Matthew Press et al. suggests that changes in readmission rates vary by condition severity.

    In an analysis of Medicare fee-for-service beneficiaries nationwide, we found that those with the highest clinical severity had readmission rates in 1997 that were approximately 6.0 percentage points higher than those in the lowest severity quartile, with this gap increasing to 8.1 percentage points by 2007 for AMI. The difference in readmission rates for the highest versus lowest severity quartiles for CHF was 5.7 percentage points in 1997 and 6.4 percentage points in 2007. This relatively increasing risk of readmission for the highest severity patients occurred despite the fact that average severity scores decreased within each severity quartile over the 10-year period. Length of stay and in-hospital mortality also declined for all patients; however, postdischarge mortality increased for the highest severity patients, whereas it decreased for the lowest severity patients.

    The authors offer two possible explanations for these findings. First, condition severity may have worsened more than observed for high severity patients and not accounted for in the risk adjustment approach applied in the analysis. That is, the highest quartile of condition severity in 2007 may have represented sicker patients than the highest quartile in 1997 in ways that were not controlled for. Sicker patients are expected to be readmitted more. Another is that actual care delivered (e.g., quality of care transitions) worsened for higher severity patients relative to lower severity patients. This might also lead to relatively more readmissions for sicker patients.

    Unfortunately, their analysis ends in 2007, well before the readmission rate reduction exhibited in aggregate since 2011 in the figure. An open question is how this downturn might have varied by condition severity.

    @afrakt

     
      Health Policy
      hospital readmissions
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  • Would the correct hospital readmissions metric please stand up? — ctd.

      06/20/2013
      Austin Frakt

    By Hillary Mull and colleagues (some of whom I work with):

    BACKGROUND: The Centers for Medicare and Medicaid Services’ (CMS) all-cause readmission measure and the 3M Health Information System Division Potentially Preventable Readmissions (PPR) measure are both used for public reporting. These 2 methods have not been directly compared in terms of how they identify high-performing and low-performing hospitals.

    OBJECTIVES: To examine how consistently the CMS and PPR methods identify performance outliers, and explore how the PPR preventability component impacts hospital readmission rates, public reporting on CMS’ Hospital Compare website, and pay-for-performance under CMS’ Hospital Readmission Reduction Program for 3 conditions (acute myocardial infarction, heart failure, and pneumonia).

    METHODS: We applied the CMS all-cause model and the PPR software to VA administrative data to calculate 30-day observed FY08-10 VA hospital readmission rates and hospital profiles. We then tested the effect of preventability on hospital readmission rates and outlier identification for reporting and pay-for-performance by replacing the dependent variable in the CMS all-cause model (Yes/No readmission) with the dichotomous PPR outcome (Yes/No preventable readmission).

    RESULTS: The CMS and PPR methods had moderate correlations in readmission rates for each condition. After controlling for all methodological differences but preventability, correlations increased to >90%. The assessment of preventability yielded different outlier results for public reporting in 7% of hospitals; for 30% of hospitals there would be an impact on Hospital Readmission Reduction Program reimbursement rates.

    CONCLUSIONS: Despite uncertainty over which readmission measure is superior in evaluating hospital performance, we confirmed that there are differences in CMS-generated and PPR-generated hospital profiles for reporting and pay-for-performance, because of methodological differences and the PPR’s preventability component.

    Prior TIE post in this area here.

    @afrakt

     
      Health Policy
      hospital readmissions
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  • MedPAC on Medicare plan competitive bidding

      06/15/2013
      Austin Frakt

    I cracked open the latest MedPAC report to read its recommendations on hospital readmissions. There wasn’t much new in it beyond what I could infer about the Commissions’ thinking in March. Still, there’s a lot more detail. You’ll find it in Chapter 4 (PDF) and the accompanying appendix (also a PDF). Some of it was reported on by Jordan Rau. All of this is ungated, which is one reason I’m giving it short shrift here.

    The other reason is that I was more surprised by what I found in Chapter 1 (PDF).

    Consistent with the goal of encouraging beneficiaries to make cost-conscious choices, this chapter presents an overview of a model based on government contributions toward purchasing Medicare coverage—an approach we call competitively determined plan contributions (CPCs). The Commission uses the term CPC to broadly describe a federal contribution toward coverage of the Medicare benefit based on the cost of competing options for the coverage, including those offered by private plans and the traditional FFS program. Specifically, CPC has two defining principles: First, beneficiaries receive a competitively determined federal contribution to buy Medicare coverage; second, beneficiaries’ individual premiums vary depending on the option they choose.

    As far as I know, this is the first time the Commission has considered Medicare plan competitive bidding (aka, premium support) — their CPC — in this way before. To be clear, it is not recommending CPCs. It’s merely exploring the idea. The chapter hits many of the issues related to competitive bidding that have been discussed on this blog (look here and here).

    Competing private plans, however, do not necessarily lower the cost to the Medicare program if the rules defining how they get paid do not encourage them to compete based on cost or premiums. For example, the current Medicare Advantage (MA) program produces a higher cost to Medicare than the traditional FFS program. Therefore, whether a CPC approach can lower overall Medicare spending depends on the specific design of the model and how different components of the model interact. […]

    Medicare Part D provides a working example of a CPC approach and illustrates the range of the detail and specificity of the rules that a CPC approach requires. […]

    The Federal Employees Health Benefits (FEHB) Program also illustrates different applications of the CPC principles.

    Again, you can read the chapter for details. There you’ll also find an exploration of these questions:

    • Should the benefit package be standardized?
    • Should a CPC model be based on competitive bidding?
    • Should a CPC model include FFS Medicare?
    • How should the federal contribution be determined?

    These are just the “first-order” questions. A presumably high-order question, “How does the federal contribution grow over time?” was raised in the report but not addressed.

    What’s interesting to me is not so much what MedPAC addressed or how they did so, since, again, I’ve covered it all here in some form. What’s interesting is that it has taken a small but significant step toward competitive bidding/premium support, not by endorsement, but just by consideration. It’s now clearly on the table for discussion by the Commission, though I don’t think this necessarily moves the political needle at all. Meanwhile, as far as I know, the Commission’s prior MA payment recommendation still stands: pay plans 100% of average fee-for-service cost.

    UPDATE: MedPAC considered competitive bidding in a 2009 report. See Chapter 7 here.

    @afrakt

     
      Health Policy
      competitive bidding, hospital readmissions, Medicare Advantage, medpac
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  • Would the correct hospital readmissions metric please stand up?

      06/12/2013
      Austin Frakt

    If hospital readmission rates measure and rank hospitals by quality, what are we to make of this?

    Objective. To quantify the differential impact on hospital performance of three readmission metrics: all-cause readmission (ACR), 3M Potential Preventable Readmission (PPR), and Centers for Medicare and Medicaid 30-day readmission (CMS).

    Data Sources. 2000–2009 California Office of Statewide Health Planning and Development Patient Discharge Data Nonpublic file.

    Study Design. We calculated 30-day readmission rates using three metrics, for three disease groups: heart failure (HF), acute myocardial infarction (AMI), and pneumonia. Using each metric, we calculated the absolute change and correlation between performance; the percent of hospitals remaining in extreme deciles and level of agreement; and differences in longitudinal performance.

    Principal Findings. Average hospital rates for HF patients and the CMS metric were generally higher than for other conditions and metrics. Correlations between the ACR and CMS metrics were highest (r = 0.67–0.84). Rates calculated using the PPR and either ACR or CMS metrics were moderately correlated (r = 0.50–0.67). Between 47 and 75 percent of hospitals in an extreme decile according to one metric remained when using a different metric. Correlations among metrics were modest when measuring hospital longitudinal change.

    Conclusions. Different approaches to computing readmissions can produce different hospital rankings and impact pay-for-performance. Careful consideration should be placed on readmission metric choice for these applications.

    That’s the abstract of the new Health Services Research study by Sheryl Davies and colleagues. The paper includes good literature reviews and thoughtful discussions in the introductory and concluding sections, covering some same work and themes found in prior TIE posts on hospital readmissions.

    @afrakt

     
      Health Policy
      hospital readmissions
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  • Readmission rates’ relation to other measures of quality

      06/10/2013
      Austin Frakt

    From “Limits Of Readmission Rates In Measuring Hospital Quality Suggest The Need For Added Metrics,” by Matthew Press et al.:

    Readmission rates in 2011 had a weak or inverse correlation with the other commonly used indicators of hospital quality (Appendix E). There were no significant differences in mean readmission rates across all quartiles of mortality rates for heart attack and pneumonia (19.8–19.9 percent and 18.4– 18.5 percent, respectively). For heart failure, mean readmission rates were significantly higher for the hospitals in the lowest mortality quartile (25.2 percent versus 24.9 percent, 24.8 percent, and 24.5 percent for the higher mortality quartiles). Results comparing the change in readmission and mortality rates longitudinally, which controls for time-invariant hospital confounders, showed a weak correlation between the two outcomes for all three conditions (Appendix F).

    Here’s what one of those plots from the appendix looks like (RSMR = risk-standardized mortality rate, RSRR = risk-standardized readmission rate):

    RSRR-RSMR-AMI

    Hospitals in the quartile with the highest composite process-measure performance had average readmission rates of 19.8 percent, 25.0 percent, and 18.4 percent for the three conditions respectively, compared with 20.0 percent, 24.9 percent, and 18.5 percent for hospitals in the quartile with the lowest performance. [] Some of the differences in readmission rates were statistically significant. However, the directionality of the trend differed across the conditions, and the differences in readmission rates were clinically insignificant, which indicates that the correlations were weak.

    The cross-sectional association between the Hospital Compare quality designations for readmission and mortality in 2011 was sometimes conflicting []. Of the hospitals designated “worse than the US national rate” for readmission for heart attack, heart failure, and pneumonia, 6 (18 percent), 41 (22 percent), and 12 (10 percent), respectively, were designated “better than the US national rate” for mortality. Of the hospitals designated “better than the US national rate” for readmission for heart attack, heart failure, and pneumonia, 0, 13 (11 percent), and 1 (2 percent), respectively, were designated as “worse than the US national rate” for mortality

    Either readmission rates are not measuring quality or they are measuring a type of quality not captured by mortality and process measures.

    UPDATE: Somewhat related, there is news out today that as readmission rates have fallen over the last few years, the number of patients placed on observation status has risen. Placement on observation isn’t counted as an admission.

    @afrakt

     
      Research Notebook
      hospital readmissions
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  • Chart of the day: Geographic variation in Medicare hospital readmission rates

      05/30/2013
      Austin Frakt

    From a recent paper by Gerhardt et al. (PDF):

    readmit HRR

    There is clear geographic clustering, with relatively lower rates in most of the western half of the country, with the exception of in and near California, and relatively higher rates in the eastern half, with the exception of the upper mid-west. I wonder to what extent this variation can be explained by disease burden vs. system factors (acknowledging the endogeneity of diagnoses).

    @afrakt

     
      Health Policy
      geographic variation, hospital readmissions, Medicare
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  • Proposed changes to the Hospital Readmissions Reduction Program (HRRP) are out

      04/29/2013
      Austin Frakt

    I told you changes were coming. The details are in the proposed rule (PDF), just out. When I can, I will read the whole thing carefully, though I hope somebody beats me to it (paging Jordan Rao). I’m also happy to crowd source (paging Ashish Jha, Karen Joynt, Brad Flansbaum, and anyone else who cares.)

    Meanwhile, I’ll just flag this bit from the pages marked 479-480:

    In accordance with section 1886(q)(5)(A) of the Act, effective for the calculation of the readmissions payment adjustment factors in FY 2015, we are proposing to expand the applicable conditions and procedures to include: (1) patients admitted for an acute exacerbation of COPD; and (2) patients admitted for elective total hip arthroplasty (THA) and total knee arthroplasty (TKA). At this point, it is not feasible for CMS to add readmission measures for three of the conditions identified by MedPAC in its 2007 Report to Congress (CABG, PCI, and other vascular conditions). We note that inpatient admissions for PCI and other vascular conditions seem to be decreasing, and these 480 procedures are being performed more in hospital outpatient departments. This shift in setting for these procedures may make their future inclusion in the Hospital Readmssion Reduction Program more difficult and impracticable. We are also exploring how we may address CABG in this program at a future time.

    Also, on the page marked 820:

    [W]e are proposing to incorporate the Planned Readmission Algorithm into the AMI, HF, PN, and Total Hip/Knee Replacement readmission measures in addition to the Hospital-Wide Readmission Measure beginning in 2013.

    I didn’t read yet about what the “planned readmission algorithm” is, though I can guess the gist. My point here is that we now know more about what CMS is planning: COPD, THA, TKA are explicitly planned to join, AMI, HF, and PN under the HRRP program. A hospital-wide (all-condition) measure appears to still be on the table.

    Comments to the proposed rule due by 5 p.m. EST on July 25, 2013.

    UPDATE: More here.

    @afrakt

     
      Health Policy
      hospital readmissions
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  • The future of Medicare’s Hospital Readmissions Reduction Program (HRRP)

      04/26/2013
      Austin Frakt

    Do you know where the HRRP is heading and when? If so, how did you find the details? I ask because I’ve had a hard time tracking them down and in writing. With the help of some sources,* I finally have. So you don’t have to, I’ve documented it all below.

    First, the program currently penalizes hospitals for high readmission rates for three conditions: acute myocardial infarction, heart failure, and pneumonia. This we all know, right?

    But what does the future hold? Turn to Section 3025 of the Payment Protection and Affordable Care Act (PPACA). It includes,

    Beginning with fiscal year 2015, the Secretary shall, to the extent practicable, expand the applicable conditions beyond the 3 conditions for which measures have been endorsed [] to the additional 4 conditions that have been identified by the Medicare Payment Advisory Commission in its report to Congress in June 2007 and to other conditions and procedures as determined appropriate by the Secretary.

    A clue! A clue! So, let’s turn to that 2007 MedPAC report, shall we? What conditions does it “identify”? They’re listed in Table 5-3.

    MedPAC readmissions

    Among them, chronic obstructive pulmonary disease (COPD), coronary artery bypass graft (CABG), percutaneous transluminal coronary angioplasty (PTCA), and other vascular conditions would be new. And, there are four of them, just like the PPACA said. It also said the Secretary may add others deemed appropriate. The scuttlebutt seems to be that an all-condition measure will be among them.

    But there’s more! QualityNet reports,**

    For 2013 public reporting, CMS has added the Hospital-Wide Readmission (HWR) measure and the readmission measure for patients undergoing elective primary total hip arthroplasty (THA) and/or total knee arthroplasty (TKA). These measures were first introduced during the September 2012 dry run.

    HWR is the all-condition measure. I had not heard about THA and TKA measures being on the table. But, if QualityNet is right, these three will be publicly reported soon.

    Should we expect public reporting to precede payment penalties? If so, then the COPD, CABG, PTCA, and other vascular conditions measures ought to be reported in 2014. And, in 2015, all of these are to be used for penalties.

    * I haven’t asked if folks who helped me find this stuff want attribution so I’m withholding it by default.

    ** I can’t tell from its site what QualityNet is or how it gets its information. It was passed to me from a source I trust, and that’s all I can say for now. UPDATE: See “About QualityNet.”

    @afrakt

     
      Health Policy
      hospital readmissions
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