In the U.S. alone, there are almost 6 million people with heart failure who rack up a collective 6.5 million hospital days per year.1
Think of it this way. Taken on average, every single heart failure patient spends at least one day per year in the hospital. And although modern medicine continues to excel at finding ways to manage heart failure, the rates of re-hospitalization are spiraling out of control. Each year, an estimated $37.2 billion is spent on heart failure.1
The Government's Solution
In 2009 CMS began tracking information on how soon Medicare patients were being readmitted to the hospital after a primary admission for congestive heart failure. They found that, on average, 25% of Medicare beneficiaries are readmitted at least once in the 30-day period following a hospital stay for heart failure.2 Sadly, Medicaid beneficiaries fared even worse.
Of special note is that for more than half of patients who rebounded to the hospital there was no bill for a visit to a doctor's office during that interim time frame.3 In other words, the patient was discharged from the hospital into the community and the next bout of medical management was a trip to the emergency department.
The Patient Protection and Affordable Care Act (ACA)4 has provisions to address the issue and in October 2012, CMS began implementation of its value-based purchasing (VBP) system.
In value-based purchasing, a natural extension of "pay for performance," hospitals are given a standard of care - for instance, a maximum "acceptable" percentage of CHF readmissions within a 30-day period - and then held to that standard to the tune of millions in financial withholdings.
If a hospital's data shows that it has failed to meet any given standard, the hospital is looking at a potential 1% reduction in Medicare payments in 2013, a 2% reduction in 2014, and a 3% reduction in 2015 and beyond.3
Obviously, the ACA has ignited a burning desire in every administrator's heart: Reduce hospital readmissions. The problem? There are few working solutions.
So what are the boots-on-the-ground solutions? How can post-discharge professionals prevent the merry-go-round of readmissions when patients are on their watch?
The National Guideline Clearinghouse's "Heart Failure in Adults" offers several recommendations for non-pharmacologic management of the HF client.5 For one, the guidelines stress the need for someone (typically a registered dietician) to provide counsel to patients about the importance of dietary restriction of salt and fluid management (water restriction). Failure to manage both dietary elements is the single most common cause for flare-ups in these patients.
Unfortunately, there is a cataclysmic disconnect between the provision of "good counsel" and proper follow-through. When a patient receives stellar advice from a qualified professional, all that has occurred is a (often one-way) discussion. It remains wholly possible for the patient to ignore, forget or otherwise dismiss every dietary pearl he received only to remember them when poised on the gurney's edge.
The same is true for most of the recommendation offered. The guidelines make a strong case for HF patients to seek proper exercise instruction, as regular activity has been shown to be of prime importance in the management of their disease. Unfortunately, even a master clinician is unable to make a single person rise up from the LazyBoy once the therapist leaves the room.
Medication management is another big issue. Guidelines make a compelling argument for having a qualified person routinely review and simplify medication regimens. This may be an excellent opportunity for an occupational therapist and nurse to work together to assist in creating a medication program that the patient can remember and routinely follow.
A final surprising recommendation from "Heart Failure in Adults" is in the realm of mental health. Studies show that most patients with HF would benefit from a screening for depression. Patients who are hospitalized are at an elevated risk for depression and should be screened. Conversely, patients who are already depressed are at a much greater risk of developing heart failure (especially older patients with hypertension). Unfortunately, there are huge societal stereotypes around mental illness and many people would rather "take their chances" than accept the label of depression.
All of these recommendations - plus the drug remedies to be provided by the physician - can be helpful in the management of a patient with HF. Unfortunately, none of them address the 800-pound gorilla in the room: Why do these patients boomerang back to the hospital so quickly?
A Paradigm Shift
This problem may be a problem of paradigm rather than a failure of innovation. The healthcare system tends to identify "risk" as the sum total of meticulously collected data points culled from inpatient records and administrative data: age, gender, clinical signs, payer source, co-morbidities and so on.3
The missing factor seems to be a discussion of environment. Healthcare providers need to ask themselves: Into what level of chaos is this patient being discharged?
Over the last few years attention has - finally, properly -- been shifting onto the importance that social instability can play in the revolving door syndrome of hospitalization.6,7 Social instability is a term of convenience used to re?ect "a relative lack of social support, education, economic stability, access to care, and safety in the patient's environment."3 For some, a shorter definition for social instability might be "life."
Establishing Predictive Models
How can these factors be quantified? Amarasingham et al attempted to create a real-time predictive model that - unlike many prior models - took frank notice of patients' post-discharge environments when attempting to calculate risk. 6
In a multivariable analysis published in 2010, the authors made the bold claim that the ability to predict who would be rehospitalized (or deceased) within 30 days would be greatly enhanced if only markers of social instability and lower socioeconomic status were included.
In their analysis, the researchers pointed to multiple (quite specific) data points that they found predicted early readmission. These red flags included factors such as: being male, being single, having a lower income, being enrolled in Medicaid, having a larger than normal number of address changes, and presenting to the emergency room during the daytime (between 6 a.m. and 6 p.m.) when other - less expensive - options for medical care were available but not utilized.6
The authors argued that these data points were easily obtainable from the patient himself or extractable from the electronic medical record within hours of admission and that there was little reason for failing to include them in any predictive model.
Another research team, Arbajie and colleagues, made one of the first stabs at clarifying the predictive factor of environmental social instability.7 Their cohort study examined patient environments to find predictors that could signal a propensity for rehospitalization.
Does the patient have a regular medical provider?
Does he require assistance to get to the doctor?
Does he live alone?
Is he married?
Is the spouse healthy?
Are there kids anywhere nearby?
Will the kids - or anyone - help with activities of daily living?
Are there basic functional needs (like showering or preparing meals) which are not met?
Are there stairs inside the house that must be navigated?
The researchers also considered more mainline factors when predicting rates of re-admission, factors like education, income and payer source, especially Medicaid enrollment.
Their conclusions were compelling.
Once they adjusted their results for demographics, health and functional status, they were able to focus on which environmental conditions led to early rehospitalization.
Patients who lived by themselves, who needed help with ADLs and did not get it, who lacked self-management skills, and who had limited education were all at higher risk of 30-day readmission. An interesting point of this analysis is the fact that after adjusting for these factors, there was no direct relationship between income and risk in this study.
The Affordable Care Act has made the reduction of readmission rates a singular goal in its pursuit of cost containment. It's relatively obvious why reducing readmission rates have captured the imagination of U.S. policymakers as a way to control costs: readmissions are common, expensive for the payer, and have a wild variability rate among hospitals.8 In short, it's an easy metric to target. But is it a wise one?
American sociologist Robert Merton popularized the concept of the law of unintended consequences in a 1936 essay, "The Unanticipated Consequences of Social Action."9 In his paper, Merton discussed how unanticipated consequences can create a perverse effect contrary to the "good" that was originally intended. He postulated that proper motives are not the only ingredient necessary for a positive outcome and that, in contrast, well-meaning efforts to provide a solution can result in a greater problem arising.
Value based purchasing is designed, in part, to punish hospitals which fail to curb their readmission rates to "acceptable" levels. But what if those rehospitalizations are neither preventable nor controllable? What if - as many experts fear 10-13 -- this law only further stratifies the gulf between the hospitals which serve the needy and those who serve the well-to-do?
We can only hope that the law of unintended consequences does not take a bad situation -- apply a healthy measure of government oversight - and make it into an intolerable one. The patient deserves better. And so do you.
Table 1. Social and environmental factors that help to predict whether a patient with CHF will be readmitted within 30 days of discharge from the hospital.3
On Social Security disability
Lives alone or in skilled nursing facility
Needs assistance with ADL (especially if does not have assistance available)
Must navigate steps in home
Lacking self-management skills (performing home exercises, taking medications properly, attending follow-up appointments, managing diet, etc.)
Lower socioeconomic status of zip code of residence
Living on less than subsistence level income (or on fixed income, like pension)
Emergency room visit between 6 a.m. and 6 p.m.
Use of a health system pharmacy
Geographic instability (multiple address changes in short period of time)
Geographic remoteness (distance from hospital services)
1. Gheorghiade M et al. (2013). Rehospitalization for heart failure: Problems and perspectives. J Am Coll Cardiol. 2013 Jan 29;61(4):391-403. doi: 10.1016/j.jacc.2012.09.038.
2. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare Fee-for-Service Program. N Engl J Med. 2009 Apr 2;360(14):1418-28. doi: 10.1056/NEJMsa0803563.
3. Hersh AM et al. (2013). Postdischarge environment following heart failure hospitalization: expanding the view of hospital readmission. J Am Heart Assoc. 2013 Apr 11;2(2):e000116. doi: 10.1161/JAHA.113.000116.
4. Patient Protection and Affordable Care Act, Pub. L. No. 111-148. §2702. 124 Stat. 119 (2010).
5. National Guideline Clearinghouse. Heart failure in adults. (2011). http://guideline.gov/content.aspx?id=34840
6. Amarasingham R, et al. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Med Care.2010 Nov;48(11):981-8. doi: 10.1097/MLR.0b013e3181ef60d9.
7. Arbaje. Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community-dwelling Medicare beneficiaries. Gerontologist. 2008 Aug;48(4):495-504.
8. Joynt KE & Jha AK. Thirty-day readmissions--truth and consequences. N Engl J Med. 2012 Apr 12;366(15):1366-9. doi: 10.1056/NEJMp1201598. Epub 2012 Mar 28.
9. Merton RK. (). The Unanticipated Consequences of Purposive Social Action. American Sociological Review. 1936 1(6): 894-904.
10. Lubell J. Hospitals cry foul, Preventable readmission penalty brings concerns. Modern Healthcare.com. 2010. http://www.modernhealthcare.com/article/20100531/MAGAZINE/100529902
11. Weinick RM & Hasnain-Wynia R. Quality improvement efforts under health reform: How to ensure that they help reduce disparities--not increase them. Health affairs (Project Hope). 2011 30(10): 1837-43.
12. Joynt KE & Rosenthal MB. Hospital value-based purchasing: will Medicare's new policy exacerbate disparities? Circ Cardiovasc Qual Outcomes. 2012 Mar 1;5(2):148-9. doi: 10.1161/CIRCOUTCOMES.112.965178.
13. Bhalla R. (2010). Could Medicare Readmission Policy Exacerbate Health Care System Inequity? Ann Intern Med. 2010 Jan 19;152(2):114-7. doi: 10.7326/0003-4819-152-2-201001190-00185. Epub 2009 Nov 30.
Andrea Salzman has more than15 years of experience in the home health, skilled nursing and outpatient setting. She creates continuing education trainings on topics such as CHF, COPD and the MI client for Care2Learn.