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.

Incentives and Work

An interesting article which is also very germane to academic physicians. In particular the sentence “what drives most academics to the university on a given day (including evenings and weekends) is not the money (otherwise we would work in the private sector) or the stability of the income stream (because the probability of losing a job is close to zero for a tenured academic). In fact, Stern (2004) shows that “scientists pay to be scientists.”  is right on the spot. One can say about physicians in academia that they “pay to be in academics”. In almost every case, they can earn quite a bit more in private practice. It is the meaning in their work that motivates them.

Artificial Intelligence & Medicine

Some recent articles ……

Goldberg JE, Rosenkrantz AB. Artificial Intelligence and Radiology: A Social Media Perspective. Current Problems in Diagnostic Radiology. 2018 Jul 23.
An interesting study examining the types of conversations on Twitter about the role of artificial intelligence in radiology. It appears that most tweets (or linked websites) were upbeat and wanted radiologists to continue supervision of AI-run diagnostics.

Meskó B, Hetényi G, Győrffy Z. Will artificial intelligence solve the human resource crisis in healthcare?. BMC health services research. 2018 Dec;18(1):545.
This article expresses the hope that artificial intelligence may be able to help in mitigating human resource crisis in health sector.

dos Santos DP, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, Maintz D, Baeßler B. Medical students' attitude towards artificial intelligence: a multicentre survey. European radiology. 2018 Jul 6:1-7.
This article examines the attitudes of undergraduate medical students towards artificial intelligence in radiology and medicine. Students think that artificial intelligence will revolutionize the practice of medicine, in particular radiological diagnosis.

Well-being–some readings

Well-being has been a focus of philosophers for centuries. While philosophers tend to question how we should live, the very answer to this question begs the question how living that way will make us better, that is, how living in a certain way will make us live well.

A little self-reflection will help you to realize that there are many things that make you live well, make you happy, excited, or content, or conversely make you feel sad. Think of the things that make you feel happy. Often these things include relationships, friends, money, accomplishments. Now think of things that make you feel sad; these may include anxiety, worry, illness, poverty. Lists of both things, things that make us happy and things that make us sad, can be long and likely will vary from person to person (at least to some extent). The question one may ask is what is it that one thing (or a small group of things) that is fundamentally deterministic of feeling well.

Hedonism is a theory of well-being which focuses on individual’s pleasure or pain. Thus, pleasure is associated with high well-being and pain is associated with poor well-being. On the other hand, perfectionism focuses on our ability to develop certain virtues or characteristics. Perfectionism is similar to eudaimonia; well-being is associated with developing virtues that are human nature. Desire theory proposes that well-being is present when one gets what one desires; in other words, fulfillment of desires is associated with well-being. Objective list theories are a set of theories that have in common a list of things that make one happy; list from one theory may not overlap with another theory. This group of theory highlight the fact that it is not easy to define what constitute well-being.

Physician White Coats and Patient Preference

An interesting study published in the BMJ Open and an interesting overview here by Brad Flansbaum.

Bottom line, formal physician attire with a white coat was rated significantly higher by patients than any other attire. Just an re-emphasis on the fact that ‘packing’ matters. One may want to to look at this that those physicians who take their profession seriously also try to wear a better representative attire than others; I am sure there a large number of physicians who may strongly disagree with this assessment.

Weak vs. Strong Social Ties

The relative contribution of the number of strong social ties versus the number of weak social ties to the health status was explored in this study. Authors examined social network characteristics as predictors of mortality in the Finnish Public Sector Study (n = 7,617) and the Health and Social Support Study (n = 20,816). At baseline, social network characteristics were surveyed. During a mean follow-up period of 16 years, participants with a small social network (≤10 members) were more likely to die than those with a large social network (≥21 members) (adjusted hazard ratio (HR) = 1.23, 95% confidence interval (CI): 1.04, 1.46). Mortality risk was increased among participants with both a small number of strong ties (≤2 members) and a small number of weak ties (≤5 members) (HR = 1.55, 95% CI: 1.26, 1.79) and among participants with both a large number of strong ties and a small number of weak ties (HR = 1.28, 95% CI: 1.08, 1.52), but not among those with a small number of strong ties and a large number of weak ties (HR = 1.04, 95% CI: 0.87, 1.25). Authors conclude that the number of weak ties may be an important component of social networks for mortality risk.