Jan 042016

By Anne Montgomery and Leslie Fried of the National Council on Aging

One of the hallmarks of the 21st century—increased longevity of the population—will increasingly drive federal, state, and local health care programs to focus on optimizing coordination of services across a range of medical care and community services providers. Discharge planning will play a central role in these efforts, particularly discharge to home.

In November, the Centers for Medicare & Medicaid Services (CMS) issued a proposed regulation for hospitals (defined as including critical access hospitals, long-term care hospitals, and inpatient rehabilitation facilities) and home health agencies. Overall, it is a solid effort and a welcome step forward in calling for Medicare and Medicaid to interface with Older Americans Act providers and disability programs under the jurisdiction of the Administration for Community Living. All of these programs and others are centrally concerned with managing beneficiaries who have complex chronic conditions. They are also well-positioned to set out criteria that can help providers establish systems of joint management of complex patients over extended periods.

However, while the rule prominently references Aging Network providers—Area Agencies on Aging, Aging Disability Resource Centers, and Centers for Independent Living—in the preamble, it does not carry substantive discussion through to actually require health care providers to coordinate with these community-based organizations.

We see this as a shortcoming, since the array of services offered by these organizations, which include home and physical environment modifications, access to assistive technologies, transportation, meals, household services, and housing support, are essential to millions of Medicare beneficiaries who cannot function day-to-day without some assistance. Absent clear requirements for health care providers to coordinate with social services organizations, risks will remain high that many fragile, complex older adults living in the community will fall through the cracks and into crisis, cycling in and out of high-cost health care settings.

If we think about discharge planning in a larger context, it is effectively only the start of a successful transition. Keeping information about patients with chronic conditions in the hands of a “sending” health care provider only, with no explicit requirements to make adequate provisions for communications and preparations with “receiving” community-based organizations, is likely to result in frustration for families and missed opportunities. By comparison, to maximize opportunities for success, CMS could bring the Aging Network into discharge planning discussions as soon as health care providers start to prepare for a patient’s transition—and could determine the actual availability of these services. If it turned out that publicly funded community services were not available (possibly due to waiting lists and/or underfunding), health care providers could be asked to explore alternatives. In all cases, figuring out what options are actually available and affordable must be done in consultation with the patient and family caregiver.

To improve the chances that community social services organizations can be better funded, we believe that CMS should direct nonprofit hospitals to assess services shortages as part of their Community Benefit Needs Assessments, and take subsequent steps to mitigate and augment critically needed services in the community. For-profit hospitals should be similarly required to work with public health offices and Aging Network providers to assess and correct any inadequacies in service supply.

On a related point—assessment of the discharge planning process—we strongly encourage CMS not to limit these reviews to assessment of the impact on readmissions (§482.43(c)(10)). Rather, in the spirit of the regulation’s frequent references to Aging Network providers, we urge that hospitals be required to establish advisory committees to conduct periodic reviews that include community social services organizations and other stakeholders in order to track the full impact of discharge planning on patient outcomes over time.

In another area—the specific elements that must be covered in discharge planning documents—the regulation proposes varying requirements for different providers. In the case of home health agencies, for example, discharge and transfer summaries must include demographic information; contact information for the physician; an advance directive, if available; the course of the illness/treatment; procedures; diagnoses; lab tests and other diagnostic testing; consultation results; a functional status assessment; a psychosocial assessment, including cognitive status; social supports; behavioral health issues; reconciliation of discharge medications; all known allergies; immunizations; smoking or nonsmoking status; vital signs; unique device identifiers for implantable devices; recommendations for ongoing care; patient goals and treatment preferences; the current plan of care, including goals, instructions, and the latest physician orders; and “any other information necessary to ensure a safe and effective transition of care that supports the post-discharge goals for the patient.”

In contrast, there is a much shorter list for critical access hospitals to consider in the context of “areas where the patient or caregiver/support person(s) would need assistance.” It includes admitting diagnosis or reason for registration, relevant co-morbidities and past medical and surgical history, anticipated ongoing care needs post-discharge, readmission risk, relevant psychosocial history, communication needs (e.g., language barriers, diminished eyesight and hearing), patients’ access to non-health care services and community-based care providers, and patients’ goals and preferences. Yet another list of criteria pertains to “discharge to home” situations, which requires instruction on post-discharge care to be used by the patient or the caregiver/support person; written information on warning signs and symptoms; prescriptions, including the name, indication, dosage, and significant risks and side effects; medication reconciliation; and written instructions for patient follow-up care, including appointments, diagnostic tests, and pertinent contact information.

Logically, there should be a list of core elements that could also be the foundation for a common care plan, and which could then be readily shared across providers working in different settings. Requiring a list of core elements would simplify care coordination and basic communication between providers, and decrease confusion and chaos for families who are often confronted suddenly with very difficult tasks when taking a seriously ill or disabled person home. Perhaps the list of required elements outlined for home health agencies could be the basis for crafting standardized core elements for all covered health care providers, along with a person’s likely future course, strengths, treatment preferences, and goals.

Concerning the critical role played by family caregivers, the rule recognizes and acknowledges the importance of families in many places – yet does not clearly establish the voluntary nature of this support: In other words, the primary consideration in discharge planning with regard to family caregivers should be to determine their willingness to provide services. To address this, we hope that CMS will consider requiring health care providers to engage in a conversation and subsequently document that a family caregiver has been asked about specific supports that he or she may need, taking into account the family’s economic resources.

The regulation features thoughtful discussion medication reconciliation and health information technology (HIT). For beneficiaries with complicated medication regimens or a track record of medication problems, we believe that CMS should encourage covered providers to use a pharmacist or physician (as compared to a software program or a nurse) whenever practicable. To make strides on HIT, there may be scope for the agency to require a standard format for recording a care plan, in order to improve interoperability and to make care plans an integral part of standards for certified electronic medical records.

Finally, the regulation should guarantee that discharge planning documents are immediately accessible to patients and family caregivers. Under current protocols governing medical records, it is often difficult for family caregivers to obtain a medical record from a hospital until after discharge, even with a patient’s signed consent (which is not always possible to get if the patient is seriously ill). This is unhelpful and counterproductive for families and should not be allowed to be extended to discharge planning documents.

CMS’ discharge planning regulation is moving in the right direction. As the agency considers these and other ideas for improvement, we hope that the agency will take the opportunity to advance a national conversation on how discharge planning can play a key role in health and social services delivery system reforms.

Dec 162014

by Stephen F. Jencks, M.D., M.P.H.

[Also see companion post by Joanne Lynn, M.D.]


The Medicare Readmission Reduction Program (MRRP) encourages hospitals to reduce readmissions within 30 days of discharge by imposing substantial financial penalties on hospitals with more readmissions than would be expected if the same patients were discharged from an average hospital.[1] But some hospitals and communities have succeeded too well and reduced discharges even more than readmissions so that their readmission rates, as currently calculated, do not improve much, which puts them at higher risk for penalties. There are two underlying problems:

First, there are two ways of thinking about, and therefore measuring, the rate of readmissions; and they often lead to quite different results and quite different decisions on penalties. One is discharge-based; the other, population-based. The relationship between the two is simple: (readmissions/discharges) X (discharges/(beneficiary population (1,000s) ) ) = readmissions / (beneficiary population (1,000s))

Patients who are admitted but die during hospitalization or are transferred to another hospital are not counted as discharges from the first hospital.

Second, effective interventions to reduce 30-day readmissions have an effect on admissions that extends far beyond 30-days after discharge and they reduce a lot of other admissions, especially if implemented in partnership with community providers and services.

When Congress created the MRRP, many stakeholders had become aware (and dismayed) that 20% of people enrolled in Medicare fee-for-service and discharged from a hospital were readmitted within 30 days of hospital discharge. Clinical trials had shown that improved processes around hospital discharges could prevent many of these readmissions. The aim of establishing accountability also made a hospital focus desirable. In this view, readmission is a burden resulting from poor hospital discharge processes, whether clinically premature or poorly executed. With that emphasis on discharge processes as cause and cure for readmissions, it was natural for the Centers for Medicare & Medicaid Services (CMS) to choose to estimate each hospital’s expected readmissions as the number of patients whom the hospital discharged and who would be expected to be readmitted after discharge from an average hospital. Most readmission reduction initiatives use this discharge-based readmission rate to measure performance. This discharge-based perspective effectively defines the readmission rate as the percentage of discharges that are followed by a readmission. In this way of thinking, the number of hospital discharges is simply a fact of life, much like the fact that a year has 365.24 days. This view does not see that hospital actions might reduce the number of patients they discharge, and this blind spot causes trouble.

Hospitals actually have a great deal of influence on how many patients they admit and discharge because so many of their discharges are admitted through their emergency department or by hospital-affiliated physicians and because they can collaborate with community services and providers who can forestall patients even coming to the hospital. Population-based hospital discharge rates vary substantially across regions, and they can change over time.

Some policy makers worried that the discharge-based rate could behave in unexpected ways if hospitals took steps that reduced total discharges by more than the reduction in 30-day readmissions. As a result, several programs, such as the Partnership for Patients and the Quality Improvement Organizations’ (QIOs) Care Transitions Program, were designed using a population-based readmission rate or converted to such a rate after evaluating early findings. The population-based rate is the number of readmissions for every 1,000 fee-for-service Medicare beneficiaries in the hospital’s service area. This view sees readmissions as a community health problem, a burden on a population of beneficiaries and the Medicare trust funds that is associated with that population’s use of hospitals just as hospital-acquired infections are associated with use of hospitals. From this perspective, preventing hospitalizations, improving discharge transitions, and improving post-discharge care are equally valid ways to reduce readmissions. Whether the hospital reduced hospitalizations in order to reduce readmissions is less important than being sure that we do not penalize hospitals for taking such steps. Population-based rates are closely aligned with the three-part aim of the National Quality Strategy (individual care, population health, and affordability), not only because they are population-based but also because they reflect the close relationship between care in the community and a hospital’s apparent performance.

Thus, a program can reduce burdens on beneficiaries and Medicare through significant reductions in the population-based discharge and readmission rates but see much smaller reductions in the discharge-based readmission rate. In a companion blog to this piece, Joanne Lynn presents evidence that this attenuation of changes in discharge-based rates has happened repeatedly in community-based readmissions programs. We do not know, at this point, whether attenuation of changes translate into financial penalties but it seems very likely to increase a hospital’s risk.

We also do yet fully understand what specific changes produce these decreases in the population-based discharge rate, but the most parsimonious explanation is that the causes are pretty much the causes of reduced readmissions: Provide urgent care with support for keeping the patient in the community, and you are likely to reduce all admissions, not just readmissions. Enroll more patients in medical homes, and the benefits will not disappear 30 days after hospital discharge. Improve nursing home communications with emergency rooms, and the benefits will not be limited to patients within 30 days after hospital discharge.

What we can foresee is that hospitals, already wary of readmissions reduction because it directly reduces revenue, will become doubly wary if they conclude that reducing discharges may also cause or increase the MRRP penalty. If CMS is penalizing hospitals and communities for succeeding at improving care and reducing costs, the reaction may threaten a very successful set of initiatives. The examples we report are for community-based efforts to reduce readmissions. Hospital-level calculations are generally beyond our capability. CMS can, however, easily determine whether, all else being equal, penalties are more likely or larger in areas where the population-based hospital discharge rate is declining substantially than elsewhere. That information is urgently needed.

What to do.

The purpose of the MRRP is to reduce the burden of readmissions on Medicare beneficiaries and the Medicare trust funds, so the important indicator of progress is the number of readmissions, not the percentage of discharged patients that are readmitted.

Healthcare quality measurement needs to catch up with the National Quality Strategy and add measures of the impact of care on the health of the population that will complement measures of the quality of individual episodes of care such as hospitalizations. In the case of readmission measurement for the MRRP, this need is substantially more urgent because there is good reason to fear that a hospital that engages with its community and does exactly what the MRRP hopes for is more liable to financial penalties under the current, discharge-based measure than it would be under a population-based measure.

The first step is to assess the degree of urgency by examining national evidence on actual penalties. If unreasonable penalties are at all frequent then the problem is far more urgent. This will be complex, because Epstein has already shown in cross-sectional studies that population-based hospitalization rates and readmission rates are positively correlated.[2] At the same time it will be important to develop population-based measures of readmissions and compare their impact on penalties with the impact of discharge-based measures. The obstacles are bureaucratic, technical, and political.

Bureaucratically, the most important obstacle has been a widespread belief that the Patient Protection and Affordable Care Act requires calculating discharge-based rates. In fact, the Act says only that penalties are to be determined from the ratio of observed to expected numbers of readmissions and is silent on how the expected number is to be calculated. The other bureaucratic problem is less tractable: Under current procedures, the steps laid out for implementing a new measure, both at CMS and at the National Quality Forum (NQF) would likely take several years. The process should be expedited if the analysis of current penalties indicates that hospitals are being penalized for success in reducing admissions.

The technical challenges of creating a population-based readmission measure for hospitals are substantial. First, the procedure must find a way to measure each hospital’s population-based hospitalization rate. Second, a method of risk adjustment must be developed and applied so that population-based readmission rates for each hospital and community can be compared. Although these methods are still evolving, adjustments for factors such as neighborhood deprivation[3] are actually easier at the population level. These are difficult tasks, but a first step good enough to improve on the existing model should be possible within a year.

Politically, hospitals will be concerned about accountability for the community hospitalization rate. They will recognize that if hospitals in areas with low hospitalization rates are protected, then hospitals in areas with high hospitalization rates will be more vulnerable.

Some have hoped that traditional risk adjustment could solve this problem, because the most likely scenario is that average risk of readmission increases as the number of discharges decreases. That prospect is not promising, because the most assiduous work on risk adjustment has produced tools of only moderate power. The prospects for solving this problem with improved risk adjustment are not promising.[4],[5]

When you find yourself in a hole you should stop digging. It seems prudent for NQF to suspend endorsement of the pending discharge-based readmission measures and for CMS to delay implementing discharge-based measures if NQF endorses them until CMS has studied and reported the extent to which readmission penalties punish hospitals that are actually reducing both admissions and readmissions and has laid out an approach to any problems found. Finally, the problem identified here underlines the importance of placing a population-based foundation under at least some measures of health care system performance.


[1] Centers for Medicare and Medicaid Services. Readmission reduction program. Retrieved from http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html

[2] Epstein, A. M., Jha, A. K., & Orav, J. E. (2011 December 15). The relationship between hospital admission rates and rehospitalizations. New England Journal of Medicine 365(24).

[3] Kind, A. J. H., Jencks, S., Brock, J., Yu, M., Bartels, C., Ehlenbach, W., & Smith, M. (2014 December 2). Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study. Annals of Internal Medicine 161(11) 765-775.

[4] Yale New Haven Health Services Corporation/Center for Outcomes Research & Evaluation. (2014, July). 2014 measure updates and specifications: Hospital-wide all-cause unplanned readmission – version 3.0. Retrieved from https://altarum.org/sites/default/files/uploaded-publication-files/Rdmsn_Msr_Updts_HWR_0714_0.pdf.

[5] Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., & Kripalani, S. (2011 October 19). Risk prediction models for hospital readmission: A systematic review. Journal of the American Medical Association 306(15) 1688-1698.

Dec 082014
Dr. Joanne Lynn Portrait

By Joanne Lynn M.D.

[Also see companion post by Stephen F. Jencks, M.D., M.P.H.]

Care transitions improvement programs have been effective in helping the health care system both become more effective in serving people living with serious chronic conditions and reduce costs. However, the key metric used to measure performance is seriously malfunctioning in at least some hospitals and communities, leading to penalties and adverse publicity for providers and communities that are actually performing well and continuing to improve performance. In this post we provide supporting data, and a companion blog article provides a thoughtful discussion of the conceptual issues underlying this troubling malfunction. For our earlier blog post about this problem see: https://medicaring.org/2014/08/26/malfunctioning-metrics/.

Very simply, this problem arises because the metric used is some variant of readmissions (within 30 days) divided by discharges (from a particular hospital) within a particular period. Thus, the usual metric is something like “20% of Medicare fee-for-service (FFS) hospitalizations are followed by a readmission within 30 days.” This metric works well if the denominator, namely the number of hospitalizations, is not affected by the improvements that reduce the risk of readmission. If the denominator declines along with the numerator, the metric will not reflect the degree of improvement that was actually achieved. The data below show that this happens in real situations.

We are here showing the data from San Diego County, a very large county with about 250,000 Medicare FFS beneficiaries, who had about 60,000 Medicare FFS admissions to hospitals per year and about 10,000 readmissions per year in 2010, when almost all of the hospitals and the county’s Aging & Independence Services (functioning as the Community-based Care Transitions Program partner agency/Area Agency on Aging/Aging and Disability Resource Center) started working together to improve care transitions and reduce readmissions under the San Diego Care Transitions Program, one of the Community-based Care Transitions Programs initiated by Section 3026 of the Patient Protection and Affordable Care Act. The application year was 2012 and the start-up year was 2013. The table below shows an initial summary of their results, provided through their Quality Improvement Organization.

Exhibit 1: San Diego County: Relative Improvement by Metric, 30-day Readmissions

Exhibit 1: San Diego County: Relative Improvement by Metric, 30-day Readmissions

Readmissions of county Medicare FFS residents fell by 15% in 2013, compared with 2010. San Diego County reduced hospitalizations by 11%. However, when the numerator and denominator go down at nearly the same rate, the fraction moves just 4.3%, which falls far short of the 20% reduction goal that Medicare has set.

What follows are the quarterly data from San Diego. The first graph, Exhibit 2, shows the quarterly rate of admissions per 1,000 Medicare FFS beneficiaries in San Diego County. We have adjusted these data for the effects of seasons on admissions (since there are usually more admissions in the winter). The shaded portion shows the “control limits,” an area which represents the expected range of variation demonstrated in the first 3 years of the data (2010-2012). Data that fall outside of the range or that consistently run on one side of the midline indicate that something has changed in how the system is functioning. Clearly, admissions are falling.

San Diego Seasonally Adjusted Admissions

Exhibit 2: San Diego Seasonally Adjusted Admissions

The second graph, Exhibit 3, shows the readmissions rate in the same framework – quarterly rate of readmissions per 1,000 Medicare FFS beneficiaries in San Diego County, adjusted for seasonality. The control limits again show change. Readmissions are falling.

Exhibit 3: Seasonally Adjusted Readmissions

Exhibit 3: Seasonally Adjusted Readmissions

The third graph, Exhibit 4, shows the metric in the conventional form, readmissions divided by discharges. The graph does eventually show a decline, but only a modest one. The fact that the denominator was falling attenuated the impact of the falling number of readmissions.

Exhibit 4: Seasonally Adjusted Percent Discharges with 30-day Readmissions for San Diego County, by quarter

Exhibit 4: Seasonally Adjusted Percent Discharges with 30-day Readmissions for San Diego County, by quarter

The next three exhibits show the comparison of the San Diego measures with the national rates for the same metrics. Exhibit 5 shows that San Diego County is dramatically less likely to have Medicare FFS beneficiaries in the hospital than the nation as a whole: 56 per 1,000 per quarter in San Diego, compared with 69 per 1,000 per quarter nationwide. Exhibit 6 shows that San Diego is also much lower in readmissions than the national average: 10 per 1,000 per quarter in San Diego, compared with 12 per 1,000 per quarter nationwide. In both cases, the declining use is reasonably parallel between San Diego and the nation. This would imply that improvement strategies are still being effective at this lower range, and thus the lower range is not yet a limit on improvement opportunities. Exhibit 7 shows that San Diego County’s conventional metric of readmissions divided by discharges simply tracks the national average. Clearly, the metric is not functioning in a way that reliably separates good practices from wasteful ones. That readmissions over discharges metric does not convey the fact that San Diego is much less likely to hospitalize and to rehospitalize. Indeed, 10 of the 14 San Diego hospitals eligible for penalties for high readmission rates are being penalized next year. Since the calculations that go into determining the hospital penalty focus on particular diagnoses in three past years, it is possible that these hospitals manage to do badly with those diagnoses in those years, but it seems quite unlikely. More plausibly, the metric used is of the readmission divided by discharge form, so the shrinking denominator will affect this calculation.

Exhibit 5: Seasonally Adjusted Quarterly Admissions, National and San Diego County

Exhibit 5: Seasonally Adjusted Quarterly Admissions, National and San Diego County

Exhibit 6: Seasonally Adjusted Quarterly Readmissions, National and San Diego County

Exhibit 6: Seasonally Adjusted Quarterly Readmissions, National and San Diego County

Exhibit 7: Percentage of Quarterly Discharges Readmitted, National and San Diego County

Exhibit 7: Percentage of Quarterly Discharges Readmitted, National and San Diego County

Without access to and analysis of much more data, one cannot know how widespread this problem is. We do know that San Francisco had an admission rate of just 50 per 1,000 per quarter in 2013 and a readmission rate of just 8 per 1,000 per quarter, which are rates much lower than San Diego. Yet 8 of San Francisco’s 10 eligible hospitals will be penalized for excessive readmissions in 2015. Furthermore, we know that the initial Medicare foray into this work, published in the Journal of the American Medical Association in January 2013 (link: http://jama.jamanetwork.com/article.aspx?articleid=1558278&resultClick=3 “Association Between Quality Improvement for Care Transitions in Communities and Rehospitalizations Among Medicare Beneficiaries”, see “Outcome Measures”), involved 14 smaller communities, and that project had to change from using the discharge-based metric to using the population-based metric when it became clear that the shrinking denominator was making the project monitoring unreliable.

Hospitals, other providers, and communities that believe they may be adversely affected by the malfunctioning metrics should have access to the data needed to investigate and CMS should welcome reconsideration of those situations. NQF should suspend endorsement of new readmission/discharge metrics and re-examing existing ones. CMS has multiple contractors working on readmissions, and some have substantial experience and skills in the technical details of these metrics. CMS should quickly modify their contracts to require them to investigate the extent of this problem, to identify steps to ameliorate adverse impacts of the current readmissions/discharges metrics, and to build the metrics that can guide care transitions work into the future. Certainly, the time has come to sort this out and develop metrics that reliably separate exemplary from persistently inefficient practices.

Want to know more?

“Protecting Hospitals that Improve Population Health” by Stephen F. Jencks.

“Senior Alert: A Swedish National Dashboard for Preventitive Care for the Elderly” by Elizabeth Rolf.

Jan 232013

By Dr. Joanne Lynn

The latest issue of JAMA features our paper describing   an exciting and successful initiative from the Centers for Medicare and Medicaid Services (CMS) and fourteen of its quality improvement organizations (QIOs).  Grounded in quality improvement methodology—plan-do-study-act–this unusual project offers many insights for those aiming to reduce avoidable readmissions.  And its raises a number of important question about how we measure progress in reducing readmissions. (For more on that topic, see our earlier MediCaring blog, https://medicaring.org/2013/01/07/readmissions-count-should-cms-revise-its-calculations/ )

A Medicare patient’s ability to receive successful treatment during care transitions from one setting to another has a crucial effect on the overall cost and efficiency of the Medicare system. Errors in information transfer, care planning or community support can cause hospitalizations, rehospitalizations and unnecessary costs to the Medicare program.

This project involved a three-year, community-based effort to improve the care transition process for fee-for-service Medicare beneficiaries. Participating QIOs facilitated cooperation among providers, health care facilities, and social services programs, such as Area Agencies on Aging. They centered their efforts around each community’s unique needs.   QIOs worked with communities to understand their own particular causes of readmissions, and to implement appropriate, evidence-based models to address them.  Communities analyzed results of the intervention along the way, and changed course to stick with interventions most likely to work.

The results, when compared to 50 comparison communities, showed significant reductions in hospitalizations and rehospitalizations, both by an almost 6% average, saving Medicare $3 million in hospitalization costs per average community per year.

This correlation has already led to new national efforts such as Partnership for Patients and the Community-based Care Transitions Program. In addition, in the 10th Scope of Work, all 53 QIOs are leading community projects nationwide (so far, in more than 400 communities).

This paper may be the first time one of America’s leading medical journals has published a report based on QI methods. Doing so represents a profound change in the openness of American medicine to learn not only what works for a patient, but works for the delivery system, too.

key words: quality improvement, care transitions, CMS, CFMC, Joanne Lynn, readmissions, community coalitions, JAMA

Jan 072013

by Dr.  Joanne Lynn

When community coalitions apply for funding from the Community-Based Care Transitions program of the Centers for Medicare and Medicaid (CMS), they have to show that they will reduce hospital readmissions by 20% and will save money for Medicare. Funding recipients will be held to those two outcomes in evaluating the contract.

In general, CMS intends to evaluate these programs by applying the 20% reduction to the rate of rehospitalization: that is, rehospitalizations/[live discharges]. If a community’s baseline rate in 2010 was 15%, then 20% of 15% is 3% and they’d have to reduce rehospitalizations to 12%.

If hospitalization itself remains stable, these are the same goal numerically.

However, much of what is done to reduce 30-day rehospitalization also reduces hospitalizations beyond 30 days, and sometimes even hospitalizations without antecedent hospitalizations. If patients learn more self-care, use more hospice, obtain more support in the community, and so forth, then the use of hospitalization outside of that 30-day window may decline as well. And it does not take a lot of decline in that rate to mimic the decline in 30-day rehospitalization, making it a challenge to change the rate of rehospitalization/hospitalization.

Suppose, for example, that a community had 10,000 hospitalizations and 1,500 30-day rehospitalizations in 2010. Suppose the CCTP work changed the rehospitalization number by a full 20% – cutting it to 1200 per year by 2014. But that good work also cut down on hospitalization by 10% — yielding 9000 for the denominator. Then 1200/9000 would be just a 13.3% rate, and the team would have missed the goal of 12% — even though it had actually done a terrific job.

It is always risky to use a rate where the denominator is presumed to be stable but actually can respond to some of the same interventions as the numerator.

Using the N of 30-day rehospitalizations has its risks also – a bad flu year or a decline in community-based support could push it up, as could an influx of patients that increases the denominator. It can also have spurious improvement if many patients are moved from FFS to managed care.

For now, it seems that the prudent thing to do is to convince CMS that they should keep the question open and make it legitimate for CCTP and providers to pursue the reduction in numbers only rather than the reduction in the rate.


key words: CCTP, readmissions rates, CMS, care transitions

Aug 232012

The P2 Collaborative of Western New York [name was changed to Population Health Collaborative in 2017] represents a different spin on the Community-based Care Transitions Program (CCTP) model. It is unique in its focus on a very rural area of Western New York, and is unusual in that it is one of a few  community-based organizations in CCTP that is NOT an Area Agency on Aging. P2 is a non-profit regional health improvement collaborative, with origins as a Robert Wood Johnson-funded Aligning Forces for Quality community project. Through that work, it has engaged in various activities within eight counties in Western New York.

As Megan Havey, Manager of Care Transitions, explains, “P2 doesn’t provide direct services, but acts as a facilitator to members of the collaborative.” The scope of the project really called for coordination by a regionally based group, one that could work with and understand the diversity of partners, and that could offer the sort of infrastructure support that such a collaborative would require.

The collaborative is one of the largest in the CMS CCTP portfolio. It includes eight local community-based organizations (CBOs) and ten hospitals, and works with other community agencies, organizations, and foundations including the Health Foundation for Western & Central New York, IPRO (the QIO), the Alzheimer’s Association, local  hospice organizations, and county health departments.  The work sprawls across seven counties, with programs that aim to serve more than 2,600 patients annually. The diversity of participating organizations is remarkable, ranging from a 5-bed to a 150-plus-bed hospital.

Over the last six years, many of the participating organizations had participated in pilot programs to improve care transitions. Other groups had little experience, but, Havey says, “…were in a great position to be mentored by groups that had experience.” In building the application, IPRO helped with many tasks, such as creating templates to conduct the required root-cause analysis, analyzing admissions data, and convening partner organizations. Havey says that although IPRO has now “stepped back” from the project, P2 continues to solicit IPRO for technical assistance and support.

The application process was instructive, Havey says, in helping the partners to appreciate just how flexible the project would need to be. “Each county had a very different target population and model,” she says. “It was important  to be able to engage partners and obtain their buy in, but also to be realistic about what we could achieve in each county. We could not create a cookie cutter model.” All of the local CBOs and hospitals are using the Coleman model, the Care Transitions Intervention™, and are targeting Medicare Fee-For-Service patients.

Havey says that developing a web-based data platform that all partners could use has been an essential step. The platform had to accommodate the range of reporting capacity partners bring to the project. To that end, P2 worked with a software company to invest in and develop a platform all hospitals could use to enter data about eligible patients. The system operates within the context of the Care Transitions Intervention, and allows care managers to document data about home and hospital visits, as well as follow-up calls and evaluation information.

Havey notes special challenges in serving a rural population, particularly in terms of accessing care. There are not enough providers, she says, and transportation to get to them can be difficult. “Rural counties have very poor health outcomes, with many medically underserved areas and populations. Our goal is to reduce readmission rates with an intervention that leads to better health outcomes and improves quality of life.”

Key words: care transitions, CCTP, Section 3026, rural residents, readmissions

Jul 232012

By Dr. Kyle Allen and Susan Hazelett

The Summa Health System/Area Agency on Aging, 10B/Geriatric Evaluation Project(SAGE) is a collaboration between an integrated health system and the local Area Agency on Aging which was begun in 1995. SAGE  provided the organizational structure to develop the resources and processes needed to effectively integrate geriatric medical services and community-based long-term care services. Such integration is essential to bridging gaps between acute medical care and community-based care, enabling medical and social services providers to reach frail older adults living in the community with multiple chronic conditions, and to improve their quality of life. The SAGE project, which operates in the Akron, Ohio, metropolitan area, has managed to do just that. Results of the 17-year collaborative indicate that consumers, health care systems, health care providers, and payers have all benefited from the focus on integrating service delivery.

In the early 1990s, Summa Health System (SHS), an integrated not-for-profit health delivery system, had launched several projects aimed at improving care for frail elders. Summa comprises six community teaching hospitals with more than 2000 beds, as well as its own health plan, skilled home care, hospice, and a foundation. Summa’s insurance plan has 150,000 covered lives, including a Medicare Advantage Plan of 23,000.One of the projects being tested at Summa was the ACE (Acute Care for Elders) model, a model of hospital care delivery aimed at improving the functional status and clinical outcomes for hospitalized older adults. Recognizing that this model did not have the necessary patient connection in the outpatient setting, Summa realized it would need to expand its reach to elderly patients across the continuum of care. To this end, it created the Center for Senior Health (CSH), an outpatient consultative service that supports primary care providers by offering an interdisciplinary, comprehensive geriatric assessment; high-risk assessment; a geriatrics resource center; a clinical teaching center; inpatient geriatric consultation and outpatient consultation followup. The CSH attempts to treat and reach the whole patient by addressing acute and chronic medical needs, psychosocial needs, and family concerns. Despite the range of services provided, the CSH continued to be limited in its scope because it did not have access to patients in their homes, nor could it provide long-term case management. As a result, it began to rely increasingly on community-based long-term care agencies for this kind of information and management.

At about the same time, the Area Agency on Aging 10B, Inc. (AAA) found itself managing a growing number of consumers with functional decline, geriatric syndromes, and multiple chronic illnesses. The AAA, which serves more than 20,000 elders in Northeast Ohio, recognized that it needed to be better integrated with the acute medical sector if it were to achieve its goal of delaying and preventing nursing home admissions.

Leaders from Summa Health and the AAA recognized the challenges and deficits each one faced in providing continuity of care to patients/consumers, and began meeting to discuss how they could build a new, integrated model of care. They realized that they shared a common goal and vision to improve care for frail elders, and launched SAGE, which provided the organizational structure needed to effectively integrate their services. SAGE had no grants or funding, just a spirit of collaboration and cooperation, and a common desire to do more than just business as usual.

A SAGE task force was created comprised of staff from both organizations, including physicians, nurses, and social workers, as well as senior leaders, to promote communication, provide feedback, and create initiatives that linked the two. The group met monthly for two years, and now meets quarterly. Among its early objectives were the development of protocols to screen and identify at-risk older adults, to establish mechanisms for information sharing and resources, to identify gaps and duplication in service delivery, to locate a AAA case manager at the CSH, to educate staff from both organizations, to collect data and information, and to identify and address barriers to implementation.

Eventually SAGE created an RN care manager assessor program, in which placed an AAA assessor in the acute care hospital. The assessor works closely with the ACE team to identify hospitalized patients who can benefit from community-based programs, as well as patients who are eligible for PASSPORT, the state’s Medicaid waiver program. This was a new initiative for the AAA, which had traditionally conducted these assessments post-discharge, in the patient’s home. That assessment now occurs before the patient is even discharged from the hospital, thus helping to determine needs for  community based services and facilitating the process for eligibility  and approval for Medicaid long term care benefits.  This is beneficial because patients will typically receive Medicare covered services for skilled needs but long term care needs are not addressed as well and the Medicare skilled benefits are provided for only a limited time usually < 30 days.   Without the other supports this vulnerable population is at risk for poor health care access, emergency department visits and  hospital readmission. The AAA then assumes case management for the consumer, and offers periodic geriatric follow-up.

This program has facilitated improved capacity management for complex patients in the acute care hospital. It improved AAA communication with primary care and hospital staff, reducing repeat hospitalizations, ED visits, and nursing home placements. It improved outcomes for complex patients, and decreased discharges from PASSPORT to nursing homes. During the pilot period,  referrals to and enrollments in the PASSPORT program doubled.   The AAA was also successful in replicating this model at other hospital systems in the Northeastern Ohio AAA service area.  A more recent positive outcome  related to this collaboration work was the awarding for AAA 10b Inc. one of the first seven  Community Based Care Transitions projects from CMS/CMMI as part of the The Community-based Care Transitions Program (CCTP), created by Section 3026 of the Patient Protection and  Affordable Care Act

In developing SAGE, several barriers had to be overcome, primarily those affecting leadership of the program, development of an effective multidisciplinary workgroup, and resources (in terms of staff time). The program can be adapted by other communities around the country, offering their acute medical system and community-based programs a way to align their services and collaborate in ways that better address the needs of frail older adults.

Key words: community collaboration, SAGE Project, ACE Units, CCTP, 3026, pilot programs

Nov 182011

CMS announced the first sites selected for the Community Based Care Transition Program. Please see the links below for the list of sites and an updated fact sheet. As noted above, we continue to accept applications and look forward to selecting additional sites in the near future.

The following overview of the selected sites offers a glimpse into where things will be happening as these programs launch. We at Medicaring.org hope to interview leaders from many of these sites, and to gain their ideas and insights about what made for a successful application, and where others might learn from their work.

The Atlanta Community-Based Care Transitions Program (Atlanta CCTP), a collaborative partnership serving ten counties in the Atlanta region, including the Atlanta Regional Commission (an Area Agency on Aging), and six urban area hospitals: Emory University Hospital Midtown, Gwinnett Medical Center, Piedmont Hospital, Southern Regional Hospital, WellStar Cobb Hospital and WellStar Kennestone Hospital.

The Akron/Canton Area Agency on Aging (A/C AAA), working in partnership with 10 acute care hospitals located within, or geographically contiguous to, the A/C AAA service area in Ohio: Affinity Hospital, Aultman Hospital, and Mercy Medical Center in Stark County; Akron General Medical Center, Summa Akron City Hospital, Summa Saint Thomas Hospital, Summa Barberton Hospital, and Summa Western Reserve Hospital in Summit County; Robinson Memorial Hospital in Portage County; and Summa Wadsworth Rittman Hospital in Medina. County.

The Southwest Ohio Care Transitions Collaborative, serving the Cincinnati Metropolitan Statistical Area and surrounding counties in Kentucky, Indiana, and Ohio, including the Council on Aging of Southwestern Ohio, the Greater Cincinnati Health Council, HealthBridge, Health Care Access Now, Healthcare Improvement Collaborative, Hamilton County Mental Health and Recovery Services Board, Clinton Memorial Hospital, Jewish Hospital, Mercy Hospital Fairfield, The Christ Hospital, and UC Health University Hospital.

The Southern Maine Agency on Aging/Aging and Disability Resource Center (SMAA/ADRC), serving five counties in southern and mid-coast Maine in partnership with the Maine Medical Center Physician-Hospital Organization and five MaineHealth hospitals: Southern Maine Medical Center, Maine Medical Center, Mid-Coast Hospital, Miles Hospital, and PenBay Medical Center.

The Area Agency on Aging, Region One, serving Maricopa County in Arizona, in partnership with John C. Lincoln North Mountain Hospital, West Valley Hospital, Scottsdale Healthcare Osborn Medical Center, John C. Lincoln Deer Valley Hospital; APIPA, a Medicaid Acute Care Plan that serves dually-enrolled Medicare fee-for-service beneficiaries; and Sunwest Pharmacy.

 Elder Services of the Merrimack Valley, Inc., in partnership with Anna Jacques Hospital, Saints Medical Center, Holy Family Hospital, Lawrence General Hospital, and Merrimack Valley Hospital, and serving 23 cities/towns in the Merrimack Valley of Massachusetts and ten bordering cities/towns in southern New Hampshire where patients using these hospitals also reside.

Council for Jewish Elderly (“CJE SeniorLife”) in Chicago, IL, partnering with Northwestern Memorial, Saint Joseph Hospital, and Saint Francis Hospital and working closely with Area Agencies on Aging in Chicago and suburbs, local Care Coordination Units (CCUs), and Illinois’ Quality Improvement Organization, IFMC.

Key words:  3026 funding, CCTP sites, care transitions, CMS

Oct 272011

 by Larry Beresford

The Hospital Association of Southern California, which convened a Palliative Care Committee to provide mutual support among its members working on palliative care initiatives, recently changed the committee’s name to the Care Transitions Committee, reflecting the affinities between these two major quality currents within America’s hospitals. But as the cover story in the most recent Quarterly newsletter of the American Association of Hospice and Palliative Medicine asks: “Where is Palliative Care in the Readmissions Boom?”

A growing body of research has documented palliative care’s ability to help seriously ill, hospitalized patients clarify their goals for treatment, manage their symptoms, and plan for the next stages of their care in alignment with their values, often at lower cost of hospital resources and higher patient satisfaction. Palliative care teams in the hospital often see the patients with the most serious illnesses, psycho-social complications and multiple chronic conditions, who are also at higher risk for readmission. Palliative care, in contrast to hospice, does not require a terminal diagnosis or time-limited prognosis. It can be offered from the point of diagnosis of a serious, chronic or incurable condition, in conjunction with any other treatment modality. Palliative care focuses on quality of life, relief of pain and suffering, and support for emotional and family concerns.

But palliative care is also serious and complex specialty care, with board certification offered in Hospice and Palliative Medicine, accredited medical fellowship opportunities, and advanced certification for hospital palliative care programs offered since September by the Joint Commission. A growing body of quality measures used in palliative care has been recognized by the National Quality Forum. Although it has been slower to develop outside the hospital’s four walls, the number of hospital-based palliative care services has steadily grown to 1,568, 63 percent of all hospitals with 50 or more beds. The same way that hospitals and hospital medicine groups are coming to recognize their responsibility for the outcomes of their discharge plans after the patient leaves the hospital, palliative care teams are now exploring their role post-discharge.

So why isn’t palliative care, with its specialty recognition and demonstrated positive outcomes, more front-and-center in current national efforts to improve care transitions across the health care system, thereby contributing to preventing unnecessary rehospitalizations? Some places, like the Hospital Association of Southern California, have acknowledged the connection. Others have given palliative care representatives a seat at the table when cross-setting teams meet to work on improving care transitions in their communities.

But Dr. Diane Meier, director of the Center to Advance Palliative Care, tells AAHPM’s Quarterly that the biggest barrier is the absence of research demonstrating the impact of palliative care consultations in the hospital on 30-day readmission rates — in contrast to data that convincingly demonstrates palliative care’s value equation within the hospital. “I think that is an urgent, high-priority research question for our field,” Dr. Meier says. “I am concerned that we are going to miss this window of opportunity, even though our patients are a big part of the readmission problem.” (For more information on palliative care, see the Center to Advance Palliative Care.)

 Key words: palliative care, care transitions, discharge planning, readmissions

Aug 082011

A colleague asked an important question: Which tools are best for reviewing causes of readmissions? Two examples, from Georgia and New Jersey, are attached to this posting. Georgia’s form requires starting from a patient/family interview review, and does not pull much from the record of the hospitalization. New Jersey’s form starts from the other direction – all pulled from charts, with just the contact information that enables an interview if someone undertakes it.  Each has targeted a certain set of issues — clear plan, medications, teach-back, advance directives, social problems, and so on.  Although the two forms overlap on many targets, on others they do not.

NJ_Readmission Chart Review tool

NEW_GA ReadmissionWorksheet

The Institute for Healthcare Improvement (IHI) has developed another useful form, which can be found on page 88 at this URL: http://www.ihi.org/knowledge/Pages/Tools/HowtoGuideImprovingTransitionstoReduceAvoidableRehospitalizations.aspx.  It “feels” more succinct, because it is set up to do 5 readmissions at a time and to focus upon themes.  But it also requires a more insightful reviewer, one who has thought about what it is that makes for rapid readmissions and what might work to make transitions bette

One way to get started is to simply review just a few charts of people who were readmitted to the hospital with which you are most familiar, and see what you most wanted to learn. You might start with the IHI form and then try filling out the other two to see what additional elements you might consider. Call a few patients or families, or, if that is not appropriate, call the main attending physician in the community. Try to gain some insight from the perspectives of people involved.
Keep track of the time it takes to do this review.  If you can get someone to pull the charts, the work to this point will take about two or three hours. Of the time involved, what seemed most productive and what was most illuminating?

Then put together your own form, starting with whichever one is most suited and adding or deleting the elements to end up with the ones that you found to be most useful.  Test that form on another two or three records, perhaps asking a colleague to do those (to learn what instructions are needed and whether another perspective identifies other things that are very important to include.
My prediction would be that you’ll find some remarkable stories–people in fragile condition whose community doctors did not really know they were out of the hospital or doctors who were unfamiliar with the patient’s situation and medications; people who could not afford the treatment prescribed; and people who simply greatly misunderstood what they were to do. (I recall the patient who told me about having to eat fresh vegetables for his heart – whereupon he opened a fresh can of peas every day!) Those stories will greatly help you galvanize the will to move ahead.  And you’ll have a process and form that you can persuade the quality improvement team at each hospital to do: Perhaps at large hospitals, five each week for four weeks and at small hospitals, five in the month.  Within a month, you’d have enough data and stories to build the endeavor, and continuing to collect the data provides rapid feedback about progress. Pick a lead intervention or two and get it tested and underway!

You are likely to find a certain sense of chaos– that there is a lot of “catch as catch can” processing with thorough unreliability on all sides. If this is the case, your coalition might well work on standardizing the process simply so that it is reliable.  You may find that the issues affecting the frail elders are different from those affecting younger populations– more complexity and fragility in the elders and more lack of access or barriers arising from mental illness in the younger.  Whatever you find, this is the “root cause analysis” that you’ll need to decide priorities and to apply for CCTP funds.

Key words: root cause analysis, reviewing readmissions, discharge record review, quality improvement tools, CCTP funding