Female internists in the US are achieving better post-discharge outcomes than males, by a margin of 3-4%. Readmission and mortality rates are half a percentage point lower when the internist is female. These are the findings of Yusuke Tsugawa, Ashish Jha and colleagues from Harvard in a recent issue of JAMA, based on a study of 1.5 million Medicare patients and almost 60,000 physicians.
MORE REINFORCED CARE BLOG ARTICLES
An article aimed at non-statisticians who would like to improve their ability to interpret and evaluate research findings, especially findings presented as evidence of cause-and-effect relationships.
Since 2010 ReInforced Care has been reaching out to patients after hospital discharge to reinforce their discharge instructions, promote their well-being, and inform hospital staff about further patient needs and experiences. Here are nine lessons we have learned through that work – some of which may surprise you.
1. Medication reconciliation is most effective if done in the home as well as in the hospital. Despite the best intentions of providers and patients, omissions are common with inpatient “med recs.” At-home med recs often turn up duplications between generics and brand-name drugs; dosage discrepancies; forgotten nutritional or herbal supplements; and failure to mention PRNs.
A research team at Boston’s Beth Israel Deaconess Medical Center has examined differences in the factors that best predict hospital readmissions occurring early in the 30-day cycle (Day 0-7) vs. later (Day 8-30). Their approach is interesting and their article is informative, but it stops short of actually quantifying the extent to which the risk of early vs. late readmission might dovetail or diverge. We extend this analysis.
Here are a few guidelines to help you better navigate those technical, quantitative studies; to pull out the most important takeaways; and to see through statements that can be misleading.
1. Think about how generalizable the results are.
Our examination of readmission penalties and the performance indicators on which they most depend.
1. Consumer health devices and mobile and telehealth initiatives. These innovative methods are extending visibility into the community, in an effort to improve care and avoid costly readmissions. New mobile health monitoring is becoming a critical tool for avoiding adverse events, shortening duration of stays, and reducing hospital readmissions. Studies have shown that patients empowered by self-tracking make healthier choices while engaging more with their health care team. These technologies will continue to improve and to bring about market-based reforms.
Notes from our session at the 2014 New England Home Care Conference
Two of us (Roland Stark and Laurie Courtney) were pleased to present a breakout session at the 2014 New England Home Care Conference. Our presentation was the result of ReInforced Care’s analysis of data from four years’ time and over 120,000 patients. The audience went away informed of some counter-intuitive results, which we call the “Home Health Conundrum,” and engaged in some spirited conversation which will help drive future research. It is our hope that the presentation left attendees thinking about Home Health’s successes; how to measure and promote those successes; whether hospital Readmission Rate (RAR) is an appropriate quality measure of Home Health agencies; and how to carry out their own research on these topics, adding to evidence-based practice in the field.
For all its sophistication, the US’s national readmission rate penalty system misses a crucial factor. For years this has been the call of thoughtful commentators such as Joynt and Jha and Sahni, Cutler, and Kocher. Now four Missouri researchers have shown in stark terms how the Centers for Medicare and Medicaid Services (CMS)’s judgments of hospital over- or under-performance would largely be abandoned after consideration of the socioeconomic status of each patient population.
In their Health Affairs article
Adding socioeconomic data to hospital readmissions calculations may produce more useful results Nagasako, Reidhead, Waterman, and Dunagan have shown that even fairly crude measures of socioeconomic status (SES) make an immense difference in each hospital’s risk-standardized readmission rate….
In an original and potentially consequential research study, Bryan Maxwell and colleagues from Stanford and Johns Hopkins have unearthed evidence that calls into question whether cardiac patients are getting the full commitment from their providers. Among 600,000 Americans receiving major cardiac surgery in recent years, the authors have found that mortality rates rise sharply just at the time when the patients would no longer be counted by the most common mortality indicator….(Continue)
We describe these myths in the hope that greater awareness and understanding of these dynamics will help health care providers make the best use of scarce resources as they support patients after discharge.
Myth 1. Hospitals that schedule a follow-up appointment before the inpatient stay is over have reliably lower readmission rates.
Appointments scheduled in this way often result in no-shows—anecdotally, as often as 60-75% of the time, depending on the hospital population. This hinders readmission reduction efforts.
Myth 2. Diagnosis is key…..(Continue)
Some eye-opening research on hospitals’ efforts to reduce heart failure readmissions–and the limits on those efforts.
A strong article by MDs Eric Alper, Terrence A O’Malley, and Jeffrey Greenwald. Covers the basic facts and issues pertaining to hospital discharge; highlights the work of other leading researchers and practitioners such as van Walraven, Naylor, Coleman, Jencks, Joynt, and Jha; and offers an excellent bibliography.
Provocative commentary on recent, paradoxical findings, and an argument against letting financial considerations trump good science. The authors both praise and go beyond the work of researchers such as Bradley, Krumholz et al.
A short, thought-provoking essay by Karen Joynt and Ashish Jha of the Harvard School of Public Health.
Three Ottawa researchers offer what may be the best explanation yet on what fraction of readmissions are avoidable/preventable. The authors cut through the murk of previous findings claiming that the percent avoidable could be as high as 79%. Walraven et al. show why the answer is closer to 23% and why this casts doubt on the wisdom of penalizing hospitals based on readmission results. The study also shows that even small reductions can signify quite significant achievements.
From America’s Health Insurance Plans, Center for Policy and Research. As the need to better track and understand readmission rates accelerates it is helpful to have a clear way to analyze the data. This article from AHIP does just that. It not only describes the methodology but gives some great examples.
It is all too easy even for peer-reviewed articles to get the math wrong. This can result in claims like this one, which could translate to a readmission rate of 162%.
Commentary by Cheryl Clark, for HealthLeaders Media.
From Health Data Management. Addresses the complexity of readmissions and the difficulty in finding solutions across disease types and systems. It is important to recognize that there is not a “magic bullet” nor a one-size-fits-all solution.
A clear and well-done overview by Hansen and colleagues from October 2011. Conclusion: No single intervention implemented alone was regularly associated with reduced risk for 30-day rehospitalization.
A thoughtful exploration of the issues, from the February 2013 Health Affairs, by Nikhil Sahni, David Cutler, and Robert Kocher.
In the March 2013 JAMA Intern Med, Donze, Aujesky, Williams, and Schnipper introduce their 7-point formula that includes some new predictors (sodium? hemoglobin?). See the link above for abstract and access to full text, or see a summary and video interview.
Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community
Walraven and colleagues discuss the value and limits of the LACE system for assigning readmission risk. From 2010.
A very clear and quite comprehensive review by Boutwell and Hwu from 2009.
Jencks, Williams, and Coleman examine readmission prevalence among different types of patients and in different US states. From 2009.