High Utilizers and Social Support

There is a common theme within the readmission reduction community that a large number of readmissions are due to limited social network around patients and only if we can provide patients with resources in community, we will be able to decrease these readmissions. Some observations studies have noticed a decrease in readmissions when patients are provided access to social/community resources, however, such studies are limited by risk of bias due to the ‘regression to the mean’ phenomenon.

Regression to mean phenomenon stipulates that if we examine participants once only performing a certain activity (or for an outcome), some of them will perform better while others will perform poorly simply due to some random factors. If we observe these participants longitudinally, we will find that those who performed well will perform poorly while those who performed poorly will perform better than their initial performance. Both groups will try to reach towards their mean (or true value).

Similarly, when we examine high-utilizer patients of health care services during a given period, those patients are likely at their worst and will do better anyway during the follow-up. This has nothing to do with the intervention but rather due to the regression to mean phenomenon. The way to address this problem is either to have several longitudinal measurements of the whole cohort where we can identify regression to the mean or to conduct a randomized clinical trial.

Finkelstein et al., conducted such a trial. They randomly assigned 800 hospitalized patients with medically and socially complex conditions with at least one additional hospitalization in the preceding 6 months, to either usual care (control group) or to the intervention group where social workers and community health workers coordinated and helped patients to access community resources.

To their, and frankly everyone else’s, surprise, they found to benefit of all the efforts of social workers and community health workers in reducing readmissions. The 180-day readmission rate was 62.3% in the intervention group and 61.7% in the control group. The adjusted between-group difference was not significant (0.82 percentage points; 95% confidence interval, −5.97 to 7.61).

More importantly, study highlighted the phenomenon of regression to the mean showing that the patients with high readmission rates resulting in enrollment generally had a decline in their readmission rate irrespective of whether they received intervention or not.

The study has few caveats but still raises very important questions – what should hospitals, healthcare systems, physicians, and other healthcare team members do to reduce readmissions and healthcare resource utilization in a very vulnerable population.

Vasodilatation in Acute Heart Failure

Acute heart failure is a condition when the cardiac output (the amount of blood pumped by heart every minutes) is not sufficient to meet the needs of the body. Cardiac output depends on how much blood is in the heart before it starts contracting (cardiac pre-load), how strongly hear contracts (cardiac contractility), how much resistance heart faces when pumping blood into the arteries (cardiac after-load, or peripheral vasoconstriction), and how many times heart beats in a minutes.

For a failing heart, increasing the force of contraction and decreasing the pressure against which it pumps blood (after-load or vasoconstriction) are important factors. In fact, peripheral vasodilators such as ACE inhibitors or ARB are standards of care for heart failure patients. One may ask, what if we decrease the pressure against which heart pumps blood really low, in other words, if we cause high (intensive) vasodilation with drugs. This particular hypothesis was tested in The GALACTIC Study recently published in JAMA.

Interestingly, authors found no benefit of intensive vasodilatation on composite endpoint of death or rehospitalization. Further, the intensive vasodilation arm had higher risk of adverse effects such as worsening renal function, hypokalemia, dizziness, and hypotension.

The results are important because they suggest that while we focus on cardiac output, blood flow to individual organ (or fraction of cardiac output received by various organs may be as important, if not more important. Intensive vasodilatation likely results in poor perfusion to various organ resulting in increased adverse effects.

Prediction of COPD Exacerbations – ACCEPT Tool

Preventing exacerbations in patients with COPD is a major goal. If we can identify patients who are at higher risk of exacerbations, we may be able to tailor more aggressive therapy to such patients. This will result in better utilization of resources, improved risk/benefit ratio, and will not expose low-risk patients to the adverse effects of aggressive therapies. Thus, predicting exacerbation risk in individual patients can guide these clinical decisions. Unfortunately, there are no externally validated and implementable tools to predict COPD exacerbation.

That is until now: Adibi and colleagues have uploaded their manuscript to a preprint server (bioRxiv) which used data from three randomized trials to develop ACCEPT, a clinical prediction tool based on routinely available predictors for COPD exacerbations. Authors externally validated ACCEPT in a large, multinational prospective cohort. ACCEPT appears to be the first COPD exacerbation prediction tool that jointly estimates the individualized rate and severity of exacerbations. The tool is designed to be easily applicable in clinical practice and is accessible as a web application.

Hospital Readmission Penalty Might be Increasing Mortality

Hospital Readmission Reduction Program and association penalties for higher than average/expected readmissions have resulted in a significant decrease in 30-day readmissions after hospital discharge. This also appears to have saved money to the CMS. When looking at the readmissions only, this programs appears to be a resounding success. However, its unintended consequences are becoming clear only now.

Using a national database of almost all hospitals, this study found that hospitals that were able to decrease readmission rates for patients with acute exacerbation of COPD, also had an increase in mortality for such patients within 30-days after discharge. While the underlying mechanisms are open for speculation, this association needs to be taken seriously and possibility of a casual relationship needs to be explored.

Hospital Visits After Discharge in Readmission Reduction Program Era

This study raises interesting questions about the effectiveness of the Hospital Readmission Reduction Program. While readmissions are going down, the hospital ED visits and observation admits are going up. Are hospitals gaming the system?

Socioeconomic Status and Readmission Penalties

CMS will be including socioeconomic status when penalizing hospitals for excess readmissions within 30-days of discharge from hospital. the socioeconomic status will be determined by calculating the percentage of population seen at a hospital that are dual eligible (both Medicaid and Medicare).  See here for the details.

There is hope that this adjustment will allow hospitals that take care of very sick patients but also socioeconomically disadvantage populations will find a more even playing field when competing with hospitals that serve relatively wealthier populations.

It is a no brainer that patients with strong support systems, easy and quick access to their primary care provider, and high health literacy are less likely to be readmitted as compared to patients who lack these factors. Often, poor support system, poverty, lack of primary care access, and limited health literacy go hand in hand. These disadvantaged patients are most difficult to treat and are higher risk of disease-related and treatment-related complications.

Now Maddox et al has calculated which hospitals are likely to benefit and how large the benefit is going to be. They found that hospitals in the lowest quintile of dual enrollment (that is hospitals in the relatively wealthier neighborhoods) saw an increase of $12.3 million in penalties, while those in the highest quintile of dual enrollment (serving disadvantaged populations) saw a decrease of $22.4 million. Large hospitals, teaching hospitals, hospitals in the most disadvantaged neighborhoods, and those with the highest proportion of beneficiaries with disabilities were markedly more likely to see a reduction in penalties, as were hospitals in states with higher Medicaid eligibility cutoffs.

Readmission Rate and Insurance Type of the Patient

One of the programs instituted through the Affordable Care Act (ACA), also known as Obamacare, is the Hospital Readmissions Reduction Program (HRRP). This program focuses on the readmission of the patients within 30-days of discharge. Hospitals that perform worse than the national average are penalized up to 3% of their Medicare payments. While the program targets Medicare beneficiaries, it is likely that changes hospitals make to reduce readmissions will benefit all patients.

An article published in Health Affairs examines this question. Authors find that the readmission rates declined after the HRRP went into effect (not surprising as it has been shown by us and many others). In addition, they also report that readmissions declined for both Medicare and Medicaid patients. However, readmission rate of Medicaid patients remained higher than Medicare patients. Readmission rate was lowest for patients who had insurance throughout the study period and declined at a similar rate compared to others.

This study highlights an important but often neglected area when examining readmissions. Readmissions depend on three main factors:

1) Hospital Discharge Practices: These are factors that are under the control of the hospital (physicians and hospital employees). These include adequate discharge instructions, adequate hand-offs to the outpatient provider, adequate education about patient illness during hospitalization, making followup appointments, etc.

2) Patient factors: These includes the severity of the underlying illness of the patient. Some patients are far sicker and even when they are discharged from hospitals, they are just beyond the edge of the need for hospitalization. A small change in patient’s condition leads to a visit (and admission) back to the hospital.

3) Social support factors: These include support from family, friends, community, and other resources.

Of these three, very little attention has been paid to the third main factor, social support. Patients with private insurance are likely to have more resources and stronger support. On the other hand, patients on Medicaid are likely to be poor with an inadequate social support system. I believe that we will start hitting the wall with regard to the readmission rate reduction and the rate of decline in readmissions will start slowing down (if it is already not slowing). Ultimately, as a society, we will have to focus on the social support aspect of readmissions to keep patients healthy and out of the hospitals.

Readmissions and Mortality–Any Difference by Race?

Since the introduction of Hospital Readmission Reduction Program by the CMS, there has been a significant decrease in readmissions for all targetted diseases. However, studies have raised concern that although readmissions are decreasing, there may be an increase in mortality.

A recent study showed that there is no difference in mortality between white and black patients. Authors used interrupted time-series analysis. I enjoyed statistical modelling but wonder if the within-hospital and between hosptial effects were properly accounted for. In other words, the assumption in their modeling is that both effects are the same (unlikely to be true). It is possible that the within and between effects are different and may shed a better light on what is happening at individual hospital level and what is happening across hospitals. Having said that, it is an interesting study with important implications.

Are Machine Learning Tools Better than Standard Tools in Predicting Readmissions

Just saw this study where authors compared standard readmission tools (HOSPITAL score, modified LACE score, and Maxim/RightCare score) with a model developed using machine learning. Authors found that machine learning score (they called it Baltimore Score or B-score) performed much better than standard tools.

While I agree that machine learning tools will likely outperform standard methods. Standard methods are quite a bit of oversimplification of the real life, machine learning tools less so. However, I doubt that authors have got their model right. Two reasons: One, their sample size is relatively small. Two, they have not validated their tool in a new dataset.

Social risk factors adjustment for readmission penalties

I have shown, in my previous work, that social risk factors affect hospital performance. CMS, at th eurging of Congress, will be including including social risk factors in their patient risk models. Here is an interesting paper that examined retrospectively the effect of adjustment for social risk factors on readmission penalties and found that the penalties will drop by almost half for safety-net hospitals. I am certain that these adjustment will bring more fairness in hospital comparisons and will decrease the amount of penalty these safety-net hospitals have to face. 

Obesity and Hospital Readmissions

The link between obesity and readmissions is not as clear cut, despite the fact that almost 40% of older adults in the US are obese. In this relatively large study examining the readmission rates of obese nursinghome patients, authors found that the relationship is perhaps complex and islikely affected by age and co-morbid conditions.

Physician Competition Effects

An interesting study examined the impact of competition among cardiologists on service provision and patients' health outcomes in the United States. Authors found that cardiologists in more concentrated markets perform more diagnostic procedures. On the other hand, higher concentration result in fewer readmissions but has no effect on mortality.

Proposed Changes to Medicare Physician Fee Schedule 2018

Last week, the Centers for Medicare & Medicaid Services (CMS) proposed a rule to update payment policies, payment rates, and quality provisions for services furnished under the Medicare Physician Fee Schedule (PFS) on or after January 1, 2019. Some of the proposed changes include:

1. Streamlining E&M payment with reduction in clinical documentation burden

2 .Recognition of services based on the use of communication technology

3. Creation of a bundled episode of care for substance abuse disorders

4. Flexibility on the use of radiology assistants

5. Removing functional status reporting requirement for outpatient treatment

6. A slight increase of $0.06 per RVU

7. Updating the direct practice expenses based on recent survey

8. Medicare telehealth services

9. Request for information on price transparency

10. Changes to the Quality Payment Program

Here is the link for those interested in learning more about these proposed changes

Medical Procedures and Heart Valve Infection

There are not free lunches! It is not uncommon to think that getting a procedure is better than not getting one, however, all procedures are associated with risks. For each procedure, the risk and benefits need to be carefully assessed before making a decision to undergo a procedure or not. Often having a procedure has greater benefits than risks, or so we assume.

While the benefits of a procedure are often clear, for example detection of coronary diseases when deciding about cardiac catheterization, the risks are not as well-studied. The list of risks associated with a procedure almost always includes risk of death and injury to some organ(s) but the actual incidence of these adverse events is usually poorly known, especially for for adverse events that are not immediately followed by the procedure (that is, there is lag-time between a procedure and associated adverse events). The more time lapses between the procedure and an adverse event, the difficult it is to ascribe adverse event to the procedure; generally so many other things, including additional procedures, happen in-between for diagnostic or therapeutic reasons.

One aspect of this issue is nicely addressed in an article published in the Journal of the American College of Cardiology where authors identified all adult patients treated for endocarditis in hospitals in Sweden and compared the occurrence of invasive medical procedures 12 weeks before endocarditis with a corresponding 12-week time period exactly 1 year earlier.

Of the 7,013 cases of infective endocarditis during the study period, several were strongly associated with cardiovascular procedures, especially coronary artery bypass grafting; procedures of the skin and management of wounds; transfusion; dialysis; bone marrow puncture; and some endoscopies, particularly bronchoscopy. A particularly interesting finding, that makes sense, is that the risk of infective endocarditis was higher if patient had a procedure while hospitalized than when patient had that procedure in outpatient setting. One way to look at this is to think that procedures should be done in outpatient setting but the converse is also possible (and more likely) that patients admitted to the hospital are sicker and hence are at higher risk of adverse effects.

Productivity Paradox and EHR

Bob Wachter and Michael Howell are optimistic about the future improvements in health information technology and its impact on physicians. I share their enthusiasm and I am optimistic about an increase in productivity in healthcare sector. And why not? Its about time.

Productivity in every other sector has resulted in decreasing utilization of human resources and increasing production of goods. However, such is not the case in healthcare. A physicians still sees about the same number of patients in a day as he/she used to see 50 or 100 years ago. It is quite possible that the number patients seen might have dropped due to documentation and other requirements. The result is evident; healthcare sector now accounts for almost one-fifth of the GDP in the US. Much of this has to so with the lack of growth in productivity in healthcare sector as compared to other sectors.

What is promising is that artificial intelligence will likely to increase productivity in the healthcare. I don’t think that this will come through improvements in health information technology (HIT). I believe it will come with better utilization of HIT as data repository for learning algorithms. Technology will also help in data capture, and it does so in many ways even now, but most importantly in data inference. However, it will not be good for an average physician.

Remember, productivity can increase only if more patients can be seen by fewer physicians (or healthcare workers). That is not possible with one physician simultaneously seeing several patients. It is possible only when machines see most patients and physician sees patients only in those situations where algorithms have not gotten enough data to develop a routine.

And what those situations will occur in relatively rare conditions. These will not our usual common diseases, what we might call ‘bread-and-butter’ medicine. Rather these will be either rare diseases or rare manifestations of common diseases. A physician overseeing such a highly productive healthcare delivery system will have to be a master clinician and a superb diagnostician.

In other words, an increase in productivity in healthcare sector will spell the doom for majority of physicians ….. happy to hear what you think……….

Hospital Readmissions and Malnutrition

While it is easy to imagine how malnutrition can be related to excess readmissions, there are not very many studies examining this association. Of note, the CMS adjustment algorithm for expected readmissions takes into account malnutrition.

This retrospective study from Australia, examined this association among 26 253 admissions, representing 19 924 patients with a 6-month readmission rate of 44.8%. They found that malnourished patients had 40% higher risk of readmission within first week after discharge and 23% higher risk of readmission between the second week and 23rd week after discharge. Authors concluded that malnutrition was a strong predictor of unplanned readmissions and suggested examination of targeted nutritional interventions to lower readmission rate. 

SES Adjustment for Readmission Penalty

CMS started implementing the Hospital Readmissions Reduction Program (HRRP) on October 1, 2013, penalizing hospitals with higher readmission rates. While readmission rates were adjusted for several factors, socioeconomic status (SES) was missing leading to protests and lobbying by hospitals caring for poor people. Hence, in 2016 the US Congress directed the CMS to to adjust for low SES. In response, CMS has made following adjustments to HRRP.

1. Make five peer groups of hospitals based on SES. SES will be determined by the proportion of “dual eligible” Medicare beneficiaries (those who are also eligible for full Medicaid benefits).
2. Calculate the median excess readmission ratios (ERRs) within each peer group to adjust for penalties
3. Scale penalties using the budget neutrality factor.

Fuller and colleagues analyzed the effect of SES adjustments (as outlined above) and examined the effect on the number and distribution of penalized hospitals. An interesting read!